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Scikit-learn : scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010. August 2013. scikit-learn 0.14 July 2014. scikit-learn 0.15.0 March 2015. scikit-learn 0.16.0 November 2015. scikit-learn 0.17.0 September 2016. scikit-learn 0.18.0 July 2017. scikit-learn 0.19.0 September 2018. scikit-learn 0.20.0 May 2019. scikit-learn 0.21.0 December 2019. scikit-learn 0.22 May 2020. scikit-learn 0.23.0 Jan 2021. scikit-learn 0.24 September 2021. scikit-learn 1.0.0 October 2021. scikit-learn 1.0.1 December 2021. scikit-learn 1.0.2 May 2022. scikit-learn 1.1.0 May 2022. scikit-learn 1.1.1 August 2022. scikit-learn 1.1.2 October 2022. scikit-learn 1.1.3 December 2022. scikit-learn 1.2.0 January 2023. scikit-learn 1.2.1 March 2023. scikit-learn 1.2.2
Scikit-learn : 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize 2022 Open Science Award for Open Source Research Software
Scikit-learn : mlpy SpaCy NLTK Orange PyTorch TensorFlow JAX Infer.NET List of numerical analysis software
Scikit-learn : Official website scikit-learn on GitHub
Scikit-multiflow : scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python.
Scikit-multiflow : scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms. It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks.
Scikit-multiflow : The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.
Scikit-multiflow : The scikit-multiflow is composed of the following sub-packages: anomaly_detection: anomaly detection methods. data: data stream methods including methods for batch-to-stream conversion and generators. drift_detection: methods for concept drift detection. evaluation: evaluation methods for stream learning. lazy: methods in which generalisation of the training data is delayed until a query is received, i.e., neighbours-based methods such as kNN. meta: meta learning (also known as ensemble) methods. neural_networks: methods based on neural networks. prototype: prototype-based learning methods. rules: rule-based learning methods. transform: perform data transformations. trees: tree-based methods, e.g. Hoeffding trees which are a type of decision tree for data streams.
Scikit-multiflow : scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community.
Scikit-multiflow : Massive Online Analysis (MOA) MEKA
Scikit-multiflow : Official website scikit-multiflow on GitHub
Self-Service Semantic Suite : The Self-Service Semantic Suite (S4) provides on-demand access to text mining and linked open data technology in the cloud. The S4 stack is based on enterprise-grade technology from Ontotext including their leading RDF engine (GraphDB, formerly OWLIM) and high performance text mining solutions successfully applied in some of the largest enterprises in the world.
Self-Service Semantic Suite : It was launched in the summer of 2014.
Self-Service Semantic Suite : S4 offers a suite of text analytics and linked data management in the cloud. You can analyze news, social media, biomedical documents and query Linked Data knowledge graphs. You can also create your own RDF knowledge graphs using GraphDB™. S4 is low cost, on demand and pay-as-you-go providing affordable, easy access to companies of any size. The RDF triplestore included with S4 is GraphDB™ which is known for scalability and query performance. GraphDB™ is the only triplestore that performs inferencing at scale. Users realize improved query speed, data availability and accurate analysis. With GraphDB it is possible to store, manage and search semantic triples extracted from S4 text mining or to create private Knowledge Graphs integrating structured and unstructured data with facts from public LOD datasets.
Self-Service Semantic Suite : All functionality of the S4 can be accessed via RESTful services. Users are provided with Getting Started guide. Also there is a complete set of documentation and sample code in JAVA, C#, Python and JavaScript.
Self-Service Semantic Suite : Presentation 4-5 Dec 2014 - LT-Accelerate Conference - Brussels == References ==
SenseTime : SenseTime is a partly state-owned publicly traded artificial intelligence company headquartered in Hong Kong. The company develops technologies including facial recognition, image recognition, object detection, optical character recognition, medical image analysis, video analysis, autonomous driving, and remote sensing. Since 2019, SenseTime has been repeatedly sanctioned by the U.S. government due to allegations that its facial recognition technology has been deployed in the surveillance and internment of the Uyghurs and other ethnic and religious minorities. SenseTime denies the allegations. The China Internet Investment Fund, a state-owned enterprise under the Cyberspace Administration of China, holds a golden share ownership stake in SenseTime.
SenseTime : In terms of security, SenseTime's technology has been used in several Chinese police departments in order to capture criminals through video footage. This is done through SenseTotem and SenseFace systems. Meitu, a popular Chinese selfie application, also uses SenseTime's technologies to modify a users' appearance. Due to concerns of its facial recognition programs being used as surveillance to ethnic Uyghurs, the U.S. Department of the Treasury's Office of Foreign Assets Control (OFAC) identified the company as a Non-SDN Chinese Military-Industrial Complex Company (NS-CMIC) in 2021.
SenseTime : Official website
Shogun (toolbox) : Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG. It is licensed under the terms of the GNU General Public License version 3 or later.
Shogun (toolbox) : The focus of Shogun is on kernel machines such as support vector machines for regression and classification problems. Shogun also offers a full implementation of Hidden Markov models. The core of Shogun is written in C++ and offers interfaces for MATLAB, Octave, Python, R, Java, Lua, Ruby and C#. Shogun has been under active development since 1999. Today there is a vibrant user community all over the world using Shogun as a base for research and education, and contributing to the core package.
Shogun (toolbox) : Currently Shogun supports the following algorithms: Support vector machines Dimensionality reduction algorithms, such as PCA, Kernel PCA, Locally Linear Embedding, Hessian Locally Linear Embedding, Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Multidimensional Scaling, Isomap, Diffusion Maps, Laplacian Eigenmaps Online learning algorithms such as SGD-QN, Vowpal Wabbit Clustering algorithms: k-means and GMM Kernel Ridge Regression, Support Vector Regression Hidden Markov Models K-Nearest Neighbors Linear discriminant analysis Kernel Perceptrons. Many different kernels are implemented, ranging from kernels for numerical data (such as gaussian or linear kernels) to kernels on special data (such as strings over certain alphabets). The currently implemented kernels for numeric data include: linear gaussian polynomial sigmoid kernels The supported kernels for special data include: Spectrum Weighted Degree Weighted Degree with Shifts The latter group of kernels allows processing of arbitrary sequences over fixed alphabets such as DNA sequences as well as whole e-mail texts.
Shogun (toolbox) : As Shogun was developed with bioinformatics applications in mind it is capable of processing huge datasets consisting of up to 10 million samples. Shogun supports the use of pre-calculated kernels. It is also possible to use a combined kernel i.e. a kernel consisting of a linear combination of arbitrary kernels over different domains. The coefficients or weights of the linear combination can be learned as well. For this purpose Shogun offers a multiple kernel learning functionality.
Shogun (toolbox) : S. Sonnenburg, G. Rätsch, S. Henschel, C. Widmer, J. Behr, A. Zien, F. De Bona, A. Binder, C. Gehl and V. Franc: The SHOGUN Machine Learning Toolbox, Journal of Machine Learning Research, 11:1799−1802, June 11, 2010. M. Gashler. Waffles: A Machine Learning Toolkit. Journal of Machine Learning Research, 12 (July):2383–2387, 2011. P. Vincent, Y. Bengio, N. Chapados, and O. Delalleau. Plearn high-performance machine learning library. URL http://plearn.berlios.de/.
Shogun (toolbox) : Shogun toolbox homepage shogun on GitHub "SHOGUN". Freecode.
Apache Spark : Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley's AMPLab starting in 2009, in 2013, the Spark codebase was donated to the Apache Software Foundation, which has maintained it since.
Apache Spark : Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged even though the RDD API is not deprecated. The RDD technology still underlies the Dataset API. Spark and its RDDs were developed in 2012 in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflow structure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory. Inside Apache Spark the workflow is managed as a directed acyclic graph (DAG). Nodes represent RDDs while edges represent the operations on the RDDs. Spark facilitates the implementation of both iterative algorithms, which visit their data set multiple times in a loop, and interactive/exploratory data analysis, i.e., the repeated database-style querying of data. The latency of such applications may be reduced by several orders of magnitude compared to Apache Hadoop MapReduce implementation. Among the class of iterative algorithms are the training algorithms for machine learning systems, which formed the initial impetus for developing Apache Spark. Apache Spark requires a cluster manager and a distributed storage system. For cluster management, Spark supports standalone native Spark, Hadoop YARN, Apache Mesos or Kubernetes. A standalone native Spark cluster can be launched manually or by the launch scripts provided by the install package. It is also possible to run the daemons on a single machine for testing. For distributed storage Spark can interface with a wide variety of distributed systems, including Alluxio, Hadoop Distributed File System (HDFS), MapR File System (MapR-FS), Cassandra, OpenStack Swift, Amazon S3, Kudu, Lustre file system, or a custom solution can be implemented. Spark also supports a pseudo-distributed local mode, usually used only for development or testing purposes, where distributed storage is not required and the local file system can be used instead; in such a scenario, Spark is run on a single machine with one executor per CPU core.
Apache Spark : Spark was initially started by Matei Zaharia at UC Berkeley's AMPLab in 2009, and open sourced in 2010 under a BSD license. In 2013, the project was donated to the Apache Software Foundation and switched its license to Apache 2.0. In February 2014, Spark became a Top-Level Apache Project. In November 2014, Spark founder M. Zaharia's company Databricks set a new world record in large scale sorting using Spark. Spark had in excess of 1000 contributors in 2015, making it one of the most active projects in the Apache Software Foundation and one of the most active open source big data projects.
Apache Spark : Big data Distributed computing Distributed data processing List of Apache Software Foundation projects List of concurrent and parallel programming languages MapReduce
Apache Spark : Official website
SPSS Modeler : IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming. One of its main aims from the outset was to eliminate needless complexity in data transformations, and make complex predictive models very easy to use. The first version incorporated decision trees (ID3), and neural networks (backprop), which could both be trained without underlying knowledge of how those techniques worked. IBM SPSS Modeler was originally named Clementine by its creators, Integral Solutions Limited. This name continued for a while after SPSS's acquisition of the product. SPSS later changed the name to SPSS Clementine, and then later to PASW Modeler. Following IBM's 2009 acquisition of SPSS, the product was renamed IBM SPSS Modeler, its current name.
SPSS Modeler : SPSS Modeler has been used in these and other industries: Customer analytics and Customer relationship management (CRM) Fraud detection and prevention Optimizing insurance claims Risk management Manufacturing quality improvement Healthcare quality improvement Forecasting demand or sales Law enforcement and border security Education Telecommunications Entertainment: e.g., predicting movie box office receipts
SPSS Modeler : IBM sells the version of SPSS Modeler 18.2.1 in two separate bundles of features. These two bundles are called "editions" by IBM: SPSS Modeler Professional: used for structured data, such as databases, mainframe data systems, flat files or BI systems SPSS Modeler Premium: Includes all the features of Modeler Professional, with the addition of: – Text analytics Both editions are available in desktop and server configurations. In addition to the traditional IBM SPSS Modeler desktop installations, IBM now offers the SPSS Modeler interface as an option in the Watson Studio product line which includes Watson Studio (cloud), Watson Studio Local, and Watson Studio Desktop. Watson Studio Desktop documentation: https://www.ibm.com/support/knowledgecenter/SSBFT6_1.1.0/mstmap/kc_welcome.html
SPSS Modeler : Early versions of the software were called Clementine and were Unix-based. The first version was released on Jun 9th 1994, after Beta testing at 6 customer sites. Clementine was originally developed by a UK company named Integral Solutions Limited (ISL), in Collaboration with artificial intelligence researchers at the University of Sussex. The original Clementine was implemented in Poplog, which ISL marketed for that University. Clementine mainly used the Poplog languages, Pop-11, with some parts written in C for speed (such as the neural network engine), along with additional tools provided as part of Solaris, VMS and various versions of Unix. The tool quickly garnered the attention of the data mining community (at that time in its infancy). In order to reach a larger market, ISL then Ported Poplog to Microsoft Windows using the NutCracker package, later named MKS Toolkit to provide the Unix graphical facilities. Original in many respects, Clementine was the first data mining tool to use an icon based graphical user interface rather than requiring users to write in a programming language, though that option remained available for expert users. In 1998 ISL was acquired by SPSS Inc., who saw the potential for extended development as a commercial data mining tool. In early 2000, the software was developed into a client–server model architecture, and shortly afterward, the client front-end interface component was rewritten fully and replaced with a new Java front-end, which allowed deeper integration with the other tools provided by SPSS. SPSS Clementine version 7.0: The client front-end runs under Windows. The server back-end Unix variants (SunOS, HP-UX, AIX), Linux, and Windows. The graphical user interface is written in Java. IBM SPSS Modeler 14.0 was the first release of Modeler by IBM. IBM SPSS Modeler 15, released in June 2012, introduced significant new functions for social network analysis and entity analytics.
SPSS Modeler : IBM SPSS Statistics List of statistical packages Cross Industry Standard Process for Data Mining
SPSS Modeler : Chapman, P.; Clinton, J.; Kerber, R.; Khabaza, T.; Reinartz, T.; Shearer, C.; Wirth, R. (2000). "CRISP-DM 1.0" (PDF). Chicago, IL: SPSS. : Cite journal requires |journal= (help) Nisbet, R.; Elder, J.; Miner, G. (2009). "Handbook of Statistical Analysis and Data Mining Applications". Burlington, MA: Academic Press (Elsevier). : Cite journal requires |journal= (help) Khabaza, Tom (1999). "The Story of Clementine" (PDF). : Cite journal requires |journal= (help)
SPSS Modeler : [1] SPSS Modeler 18.2.1 Documentation Users Guide – SPSS Modeler 18.2.1 IBM SPSS Modeler website
Apache SystemDS : Apache SystemDS (Previously, Apache SystemML) is an open source ML system for the end-to-end data science lifecycle. SystemDS's distinguishing characteristics are: Algorithm customizability via R-like and Python-like languages. Multiple execution modes, including Standalone, Spark Batch, Spark MLContext, Hadoop Batch, and JMLC. Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.
Apache SystemDS : SystemML was created in 2010 by researchers at the IBM Almaden Research Center led by IBM Fellow Shivakumar Vaithyanathan. It was observed that data scientists would write machine learning algorithms in languages such as R and Python for small data. When it came time to scale to big data, a systems programmer would be needed to scale the algorithm in a language such as Scala. This process typically involved days or weeks per iteration, and errors would occur translating the algorithms to operate on big data. SystemML seeks to simplify this process. A primary goal of SystemML is to automatically scale an algorithm written in an R-like or Python-like language to operate on big data, generating the same answer without the error-prone, multi-iterative translation approach. On June 15, 2015, at the Spark Summit in San Francisco, Beth Smith, General Manager of IBM Analytics, announced that IBM was open-sourcing SystemML as part of IBM's major commitment to Apache Spark and Spark-related projects. SystemML became publicly available on GitHub on August 27, 2015 and became an Apache Incubator project on November 2, 2015. On May 17, 2017, the Apache Software Foundation Board approved the graduation of Apache SystemML as an Apache Top Level Project.
Apache SystemDS : The following are some of the technologies built into the SystemDS engine. Compressed Linear Algebra for Large Scale Machine Learning Declarative Machine Learning Language
Apache SystemDS : SystemDS 2.0.0 is the first major release under the new name. This release contains a major refactoring, a few major features, a large number of improvements and fixes, and some experimental features to better support the end-to-end data science lifecycle. In addition to that, this release also removes several features that are not up date and outdated. New mechanism for DML-bodied (script-level) builtin functions, and a wealth of new built-in functions for data preprocessing including data cleaning, augmentation and feature engineering techniques, new ML algorithms, and model debugging. Several methods for data cleaning have been implemented including multiple imputations with multivariate imputation by chained equations (MICE) and other techniques, SMOTE, an oversampling technique for class imbalance, forward and backward NA filling, cleaning using schema and length information, support for outlier detection using standard deviation and inter-quartile range, and functional dependency discovery. A complete framework for lineage tracing and reuse including support for loop deduplication, full and partial reuse, compiler assisted reuse, several new rewrites to facilitate reuse. New federated runtime backend including support for federated matrices and frames, federated builtins (transform-encode, decode etc.). Refactor compression package and add functionalities including quantization for lossy compression, binary cell operations, left matrix multiplication. [experimental] New python bindings with supports for several builtins, matrix operations, federated tensors and lineage traces. Cuda implementation of cumulative aggregate operators (cumsum, cumprod etc.) New model debugging technique with slice finder. New tensor data model (basic tensors of different value types, data tensors with schema) [experimental] Cloud deployment scripts for AWS and scripts to set up and start federated operations. Performance improvements with parallel sort, gpu cum agg, append cbind etc. Various compiler and runtime improvements including new and improved rewrites, reduced Spark context creation, new eval framework, list operations, updated native kernel libraries to name a few. New data reader/writer for json frames and support for sql as a data source. Miscellaneous improvements: improved documentation, better testing, run/release scripts, improved packaging, Docker container for systemds, support for lambda expressions, bug fixes. Removed MapReduce compiler and runtime backend, pydml parser, Java-UDF framework, script-level debugger. Deprecated ./scripts/algorithms, as those algorithms gradually will be part of SystemDS builtins.
Apache SystemDS : Apache SystemDS welcomes contributions in code, question and answer, community building, or spreading the word. The contributor guide is available at https://github.com/apache/systemds/blob/main/CONTRIBUTING.md
Apache SystemDS : Comparison of deep learning software
Apache SystemDS : Apache SystemML website IBM Research - SystemML Q & A with Shiv Vaithyanathan, Creator of SystemML and IBM Fellow A Universal Translator for Big Data and Machine Learning SystemML: Declarative Machine Learning at Scale presentation by Fred Reiss SystemML: Declarative Machine Learning on MapReduce Archived 2016-03-10 at the Wayback Machine Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs IBM's SystemML machine learning system becomes Apache Incubator project IBM donates machine learning tech to Apache Spark open source community IBM's SystemML Moves Forward as Apache Incubator Project
Tanagra (machine learning) : Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and association rule learning. Tanagra is an academic project. It is widely used in French-speaking universities. Tanagra is frequently used in real studies and in software comparison papers.
Tanagra (machine learning) : The development of Tanagra was started in June 2003. The first version was distributed in December 2003. Tanagra is the successor of Sipina, another free data mining tool which is intended only for supervised learning tasks (classification), especially the interactive and visual construction of decision trees. Sipina is still available online and is maintained. Tanagra is an "open source project" as every researcher can access the source code and add their own algorithms, as long as they agree and conform to the software distribution license. The main purpose of the Tanagra project is to give researchers and students a user-friendly data mining software, conforming to the present norms of the software development in this domain (especially in the design of its GUI and the way to use it), and allowing the analyzation of either real or synthetic data. From 2006, Ricco Rakotomalala made an important documentation effort. A large number of tutorials are published on a dedicated website. They describe the statistical and machine learning methods and their implementation with Tanagra on real case studies. The use of other free data mining tools on the same problems is also widely described. The comparison of the tools enables readers to understand the possible differences in the presentation of results.
Tanagra (machine learning) : Tanagra works similarly to current data mining tools. The user can design visually a data mining process in a diagram. Each node is a statistical or machine learning technique, the connection between two nodes represents the data transfer. But unlike the majority of tools which are based on the workflow paradigm, Tanagra is very simplified. The treatments are represented in a tree diagram. The results are displayed in an HTML format. This makes it is easy to export the outputs in order to visualize the results in a browser. It is also possible to copy the result tables to a spreadsheet. Tanagra makes a good compromise between statistical approaches (e.g. parametric and nonparametric statistical tests), multivariate analysis methods (e.g. factor analysis, correspondence analysis, cluster analysis, regression) and machine learning techniques (e.g. neural network, support vector machine, decision trees, random forest).
Tanagra (machine learning) : Free statistical software Data mining List of numerical analysis software
Tanagra (machine learning) : Tanagra Project home page Sipina Project home page Free Statistical Software on StatPages.net
List of text mining software : Text mining computer programs are available from many commercial and open source companies and sources.
List of text mining software : Angoss – Angoss Text Analytics provides entity and theme extraction, topic categorization, sentiment analysis and document summarization capabilities via the embedded AUTINDEX – is a commercial text mining software package based on sophisticated linguistics by IAI (Institute for Applied Information Sciences), Saarbrücken. DigitalMR – social media listening & text+image analytics tool for market research. FICO Score – leading provider of analytics. General Sentiment – Social Intelligence platform that uses natural language processing to discover affinities between the fans of brands with the fans of traditional television shows in social media. Stand alone text analytics to capture social knowledge base on billions of topics stored to 2004. IBM LanguageWare – the IBM suite for text analytics (tools and Runtime). IBM SPSS – provider of Modeler Premium (previously called IBM SPSS Modeler and IBM SPSS Text Analytics), which contains advanced NLP-based text analysis capabilities (multi-lingual sentiment, event and fact extraction), that can be used in conjunction with Predictive Modeling. Text Analytics for Surveys provides the ability to categorize survey responses using NLP-based capabilities for further analysis or reporting. Inxight – provider of text analytics, search, and unstructured visualization technologies. (Inxight was bought by Business Objects that was bought by SAP AG in 2008). Language Computer Corporation – text extraction and analysis tools, available in multiple languages. Lexalytics – provider of a text analytics engine used in Social Media Monitoring, Voice of Customer, Survey Analysis, and other applications. Salience Engine. The software provides the unique capability of merging the output of unstructured, text-based analysis with structured data to provide additional predictive variables for improved predictive models and association analysis. Linguamatics – provider of natural language processing (NLP) based enterprise text mining and text analytics software, I2E, for high-value knowledge discovery and decision support. Mathematica – provides built in tools for text alignment, pattern matching, clustering and semantic analysis. See Wolfram Language, the programming language of Mathematica. MATLAB offers Text Analytics Toolbox for importing text data, converting it to numeric form for use in machine and deep learning, sentiment analysis and classification tasks. Medallia – offers one system of record for survey, social, text, written and online feedback. NetOwl – suite of multilingual text and entity analytics products, including entity extraction, link and event extraction, sentiment analysis, geotagging, name translation, name matching, and identity resolution, among others. PolyAnalyst - text analytics environment. PoolParty Semantic Suite - graph-based text mining platform. RapidMiner with its Text Processing Extension – data and text mining software. SAS – SAS Text Miner and Teragram; commercial text analytics, natural language processing, and taxonomy software used for Information Management. Sketch Engine – a corpus manager and analysis software which providing creating text corpora from uploaded texts or the Web including part-of-speech tagging and lemmatization or detecting a particular website. Sysomos – provider social media analytics software platform, including text analytics and sentiment analysis on online consumer conversations. WordStat – Content analysis and text mining add-on module of QDA Miner for analyzing large amounts of text data.
List of text mining software : Carrot2 – text and search results clustering framework. GATE – general Architecture for Text Engineering, an open-source toolbox for natural language processing and language engineering. Gensim – large-scale topic modelling and extraction of semantic information from unstructured text (Python). KH Coder – for Quantitative Content Analysis or Text Mining The KNIME Text Processing extension. Natural Language Toolkit (NLTK) – a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python programming language. OpenNLP – natural language processing. Orange with its text mining add-on. The PLOS Text Mining Collection. The programming language R provides a framework for text mining applications in the package tm. The Natural Language Processing task view contains tm and other text mining library packages. spaCy – open-source Natural Language Processing library for Python Stanbol – an open source text mining engine targeted at semantic content management. Voyant Tools – a web-based text analysis environment, created as a scholarly project.
List of text mining software : Text Mining APIs on Mashape Text Mining APIs on Programmable Web Text Mining APIs at the Text Analysis Portal for Research
UIMA : UIMA ( yoo-EE-mə), short for Unstructured Information Management Architecture, is an OASIS standard for content analytics, originally developed at IBM. It provides a component software architecture for the development, discovery, composition, and deployment of multi-modal analytics for the analysis of unstructured information and integration with search technologies.
UIMA : The UIMA architecture can be thought of in four dimensions: It specifies component interfaces in an analytics pipeline. It describes a set of design patterns. It suggests two data representations: an in-memory representation of annotations for high-performance analytics and an XML representation of annotations for integration with remote web services. It suggests development roles allowing tools to be used by users with diverse skills.
UIMA : Apache UIMA, a reference implementation of UIMA, is maintained by the Apache Software Foundation. UIMA is used in a number of software projects: IBM Research's Watson uses UIMA for analyzing unstructured data. The Clinical Text Analysis and Knowledge Extraction System (Apache cTAKES) is a UIMA-based system for information extraction from medical records. DKPro Core is a collection of reusable UIMA components for general-purpose natural language processing.
UIMA : Data Discovery and Query Builder Entity extraction General Architecture for Text Engineering (GATE) IBM Omnifind LanguageWare
UIMA : Apache UIMA home page
VITAL (machine learning software) : VITAL (Validating Investment Tool for Advancing Life Sciences) was a Board Management Software machine learning proprietary software developed by Aging Analytics, a company registered in Bristol (England) and dissolved in 2017. Andrew Garazha (the firm's Senior Analyst) declared that the project aimed "through iterative releases and updates to create a piece of software capable of making autonomous investment decisions." According to Nick Dyer-Witheford, VITAL 1.0 was a "basic algorithm". On 13 May 2014, Deep Knowledge Ventures, a Hong Kong venture capital firm, claimed to have appointed VITAL to its board of directors in order to prove that artificial intelligence could be an instrument for investment decision-making. The announcement received great press coverage despite the fact commentators consider this a publicity stunt. Fortune reported in 2019 that VITAL is no longer used.
VITAL (machine learning software) : Academics and journalists viewed VITAL's board appointment with skepticism. University of Sheffield computer science professor Noel Sharkey called it "a publicity hype". Michael Osborne, a University of Oxford associate professor in machine learning, found it is "a gimmick to call that an actual board member". Simon Sharwood of The Register, wrote there is "a strong whiff of stunt and/or promotion about this". In a 2019 speech, the Chief Scientist of Australia, Alan Finkel, commented, "At the time, most of us probably dismissed Vital as a PR exercise. I admit, I used her story three years ago to get a laugh in one of my speeches." Florian Möslein, a law professor at the University of Marburg, wrote in 2018 that "Vital has widely been acknowledged as the 'world's first artificial intelligence company director'". Vice journalist Jason Koebler suggested that the software did not have any article intelligence capabilities and concluded "VITAL can’t talk, and it can’t hear, and it can’t be a real, functional executive of a company." Sharwood of The Register noted that because VITAL was not a natural person, it could not be a board member under Hong Kong's corporate governance laws. However, in a 2017 interview to The Nikkei, Dmitry Kaminskiy, managing partner of Deep Knowledge Ventures, stated that VITAL had observer status on the board and no voting rights. University of Sheffield computer science professor Noel Sharkey said of VITAL, "On first sight, it looks like a futuristic idea but on reflection it is really a little bit of publicity hype." Vice journalist Jason Koebler said "this is a gimmick" and said "There is literally nothing to suggest that VITAL has any sort of capabilities beyond any other proprietary analysis software". Michael Osborne, a University of Oxford associate professor in machine learning, found VITAL's appointment to be noncredible, saying it is "a bit of a gimmick to call that an actual board member". Osborne said that a core duty of board members to converse with each other, which the algorithm is incapable of doing, so its more likely functionality is to serve as a springboard for conversation among other board members. In a 2019 speech, the Chief Scientist of Australia, Alan Finkel, commented, "At the time, most of us probably dismissed Vital as a PR exercise. I admit, I used her story three years ago to get a laugh in one of my speeches."
VITAL (machine learning software) : VITAL was created by a group of programmers employed by Aging Analytics According to Andrew Garazh, Aging Analytics Senior Analyst, VITAL was not a machine learning algorithm as the necessary datasets on investment rounds, intellectual property and clinical trial outcomes are generally not disclosed. Rather, VITAL used fuzzy logic based on 50 parameters to assess risk factors. Aging Analytics licensed the software to Deep Knowledge Ventures. It was used to help the human board members of Deep Knowledge Venture make investment decisions in biotechnology companies. For instance, it supported investments in Insilico Medicine, which creates ways for computers to help find drugs in research into aging. VITAL also supported investing in Pathway Pharmaceuticals, which uses the OncoFinder algorithm to choose and appraise cancer treatments. According to Dmitry Kaminskiy, managing partner of Deep Knowledge Ventures, the motivation for using VITAL was the large number of failed investments in the biotechnology sector and the desire to avoid investing in companies likely to fail.
VITAL (machine learning software) : Scholars addressed questions around the safety, privacy, accountability transparency and bias in algorithms. Writing in the philosophical journal Multitudes, the academic Ariel Kyrou raised questions about the consequences of a mistake made by an algorithm recommending a dangerous investment. He raised the hypothetical where VITAL was able to persuade the board to invest in a startup that had the facade of doing research into treatment for age-associated ills, but in actuality was run by terrorists who were raising funds. Kyrou raised a series of questions about who society would fault for VITAL's mistake. As the owner of VITAL, should Deep Knowledge Ventures be held accountable, or rather should the companies that supplied data to VITAL or the people who created VITAL be held liable? Simon Sharwood of The Register wrote that because the appointment of a software program to the board directors is not legally feasible in Hong Kong, there is "a strong whiff of stunt and/or promotion about this". Quoting a Thomson Reuters website describing Hong Kong legislation related to corporate governance, Sharwood pointed out that in Hong Kong "the board comprises all of the directors of the company" and "a director must normally be a natural person, except that a private company may have a body corporate as its director if the company is not a member of a listed group." He concluded that since VITAL cannot be considered a "natural person", it is merely a "cosmetic" appointment to the board and that "this software is no more a Board member than Caligula's horse was a senator". Sharwood further argued that corporations frequently purchase directors and officers liability insurance but that it would be practically impossible to get such insurance for VITAL. Sharwood also wrote that were VITAL to be hacked, any misinformation it outputs could be considered "false and misleading communications". In the book Research Handbook on the Law of Artificial Intelligence, Florian Mölein wrote that VITAL could not become a director as defined in Hong Kong's corporate laws, so the other directors just were approaching it as "a member of [the] board with observer status". Lin Shaowei raised concerns in a Journal of East China University of Political Science and Law article about how the software's appearance inspired a complex question about the relationship between corporate law and artificial intelligence. VITAL could be considered either a board director who has voting rights or an observer who does not. Lin said either choice raised questions about whether VITAL is subject to corporate law and who would be held accountable if VITAL recommends a choice that turns out to be damaging to the company. David Theo Goldberg in the Critical Times, a peer reviewed journal in Critical Global Theory, argues that VITAL processed a dataset to predict the most remunerative investment opportunities. Drawing his analysis on an article from Business Insider, Goldberg describes VITAL's decision-making predictiveness based "on surface pattern recognition and the identification of regularities and/or irregularities". In other words, Goldberg asserts that "the normativity of the surface" explains algorithmic knowledge of a "product" like VITAL. In Homo Deus, Yuval Noah Harari mentions VITAL as an example of the future risks that humankind faces. Harari argues that the human mind is being replaced by a world in which algorithms and data make the decisions. Specifically, it is argued that "as algorithms push humans out of the job market," executive boards driven by artificial intelligence are more likely to give priority to algorithms over the humans. == References ==
Vowpal Wabbit : Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford. Vowpal Wabbit's interactive learning support is particularly notable including Contextual Bandits, Active Learning, and forms of guided Reinforcement Learning. Vowpal Wabbit provides an efficient scalable out-of-core implementation with support for a number of machine learning reductions, importance weighting, and a selection of different loss functions and optimization algorithms.
Vowpal Wabbit : The VW program supports: Multiple supervised (and semi-supervised) learning problems: Classification (both binary and multi-class) Regression Active learning (partially labeled data) for both regression and classification Multiple learning algorithms (model-types / representations) OLS regression Matrix factorization (sparse matrix SVD) Single layer neural net (with user specified hidden layer node count) Searn (Search and Learn) Latent Dirichlet Allocation (LDA) Stagewise polynomial approximation Recommend top-K out of N One-against-all (OAA) and cost-sensitive OAA reduction for multi-class Weighted all pairs Contextual-bandit (with multiple exploration/exploitation strategies) Multiple loss functions: squared error quantile hinge logistic poisson Multiple optimization algorithms Stochastic gradient descent (SGD) BFGS Conjugate gradient Regularization (L1 norm, L2 norm, & elastic net regularization) Flexible input - input features may be: Binary Numerical Categorical (via flexible feature-naming and the hash trick) Can deal with missing values/sparse-features Other features On the fly generation of feature interactions (quadratic and cubic) On the fly generation of N-grams with optional skips (useful for word/language data-sets) Automatic test-set holdout and early termination on multiple passes bootstrapping User settable online learning progress report + auditing of the model Hyperparameter optimization
Vowpal Wabbit : Vowpal wabbit has been used to learn a tera-feature (1012) data-set on 1000 nodes in one hour. Its scalability is aided by several factors: Out-of-core online learning: no need to load all data into memory The hashing trick: feature identities are converted to a weight index via a hash (uses 32-bit MurmurHash3) Exploiting multi-core CPUs: parsing of input and learning are done in separate threads. Compiled C++ code
Vowpal Wabbit : Official website Vowpal Wabbit's github repository Documentation and examples (github wiki) Vowpal Wabbit Tutorial at NIPS 2011 Questions (and answers) tagged 'vowpalwabbit' on StackOverflow
Waffles (machine learning) : Waffles is a collection of command-line tools for performing machine learning operations developed at Brigham Young University. These tools are written in C++, and are available under the GNU Lesser General Public License.
Waffles (machine learning) : The Waffles machine learning toolkit contains command-line tools for performing various operations related to machine learning, data mining, and predictive modeling. The primary focus of Waffles is to provide tools that are simple to use in scripted experiments or processes. For example, the supervised learning algorithms included in Waffles are all designed to support multi-dimensional labels, classification and regression, automatically impute missing values, and automatically apply necessary filters to transform the data to a type that the algorithm can support, such that arbitrary learning algorithms can be used with arbitrary data sets. Many other machine learning toolkits provide similar functionality, but require the user to explicitly configure data filters and transformations to make it compatible with a particular learning algorithm. The algorithms provided in Waffles also have the ability to automatically tune their own parameters (with the cost of additional computational overhead). Because Waffles is designed for script-ability, it deliberately avoids presenting its tools in a graphical environment. It does, however, include a graphical "wizard" tool that guides the user to generate a command that will perform a desired task. This wizard does not actually perform the operation, but requires the user to paste the command that it generates into a command terminal or a script. The idea motivating this design is to prevent the user from becoming "locked in" to a graphical interface. All of the Waffles tools are implemented as thin wrappers around functionality in a C++ class library. This makes it possible to convert scripted processes into native applications with minimal effort. Waffles was first released as an open source project in 2005. Since that time, it has been developed at Brigham Young University, with a new version having been released approximately every 6–9 months. Waffles is not an acronym—the toolkit was named after the food for historical reasons.
Waffles (machine learning) : Some of the advantages of Waffles in contrast with other popular open source machine learning toolkits include: Waffles automatically takes care of many issues related to data format in order to simplify its tools. Because it is implemented in C++, many of its algorithms are particularly fast. Also, the lack of dependency on any virtual machine makes it easier to deploy in conjunction with other applications. The functionality included in Waffles is very broad, including algorithms for dimensionality reduction, collaborative filtering, visualization, clustering, supervised learning, optimization, linear algebra, data transformation, image and signal processing, policy learning, and sparse matrix operations.
Waffles (machine learning) : Although Waffles provides significant breadth, it lacks the depth of many toolkits that focus on a particular area of machine learning. The Weka (machine learning) toolkit, for example, provides many more classification algorithms than Waffles provides. Waffles only has a limited graphical interface.
Waffles (machine learning) : Weka (machine learning) RapidMiner (formerly YALE (Yet Another Learning Environment)), a commercial machine learning framework implemented in Java List of numerical analysis software == References ==
Weka (software) : Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
Weka (software) : Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include: Free availability under the GNU General Public License. Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform. A comprehensive collection of data preprocessing and modeling techniques. Ease of use due to its graphical user interfaces. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. Input to Weka is expected to be formatted according the Attribute-Relational File Format and with the filename bearing the .arff extension. All of Weka's techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes (normally, numeric or nominal attributes, but some other attribute types are also supported). Weka provides access to SQL databases using Java Database Connectivity and can process the result returned by a database query. Weka provides access to deep learning with Deeplearning4j. It is not capable of multi-relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka. Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.
Weka (software) : In version 3.7.2, a package manager was added to allow the easier installation of extension packages. Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions.
Weka (software) : In 1993, the University of Waikato in New Zealand began development of the original version of Weka, which became a mix of Tcl/Tk, C, and makefiles. In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms. In 2005, Weka received the SIGKDD Data Mining and Knowledge Discovery Service Award. In 2006, Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence. It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. Pentaho has since been acquired by Hitachi Vantara, and Weka now underpins the PMI (Plugin for Machine Intelligence) open source component.
Weka (software) : Auto-WEKA is an automated machine learning system for Weka. Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods. H2O.ai is an open-source data science and machine learning platform KNIME is a machine learning and data mining software implemented in Java. Massive Online Analysis (MOA) is an open-source project for large scale mining of data streams, also developed at the University of Waikato in New Zealand. Neural Designer is a data mining software based on deep learning techniques written in C++. Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn. RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka. scikit-learn is a popular machine learning library in Python.
Weka (software) : List of numerical-analysis software
Weka (software) : Official website at University of Waikato in New Zealand
Wolfram Mathematica : Wolfram Mathematica is a software system with built-in libraries for several areas of technical computing that allows machine learning, statistics, symbolic computation, data manipulation, network analysis, time series analysis, NLP, optimization, plotting functions and various types of data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other programming languages. It was conceived by Stephen Wolfram, and is developed by Wolfram Research of Champaign, Illinois. The Wolfram Language is the programming language used in Mathematica. Mathematica 1.0 was released on June 23, 1988 in Champaign, Illinois and Santa Clara, California. Mathematica's Wolfram Language is fundamentally based on Lisp; for example, the Mathematica command Most is identically equal to the Lisp command butlast. There is a substantial literature on the development of computer algebra systems (CAS).
Wolfram Mathematica : Mathematica is split into two parts: the kernel and the front end. The kernel interprets expressions (Wolfram Language code) and returns result expressions, which can then be displayed by the front end. The original front end, designed by Theodore Gray in 1988, consists of a notebook interface and allows the creation and editing of notebook documents that can contain code, plaintext, images, and graphics. Code development is also supported through support in a range of standard integrated development environment (IDE) including Eclipse, IntelliJ IDEA, Atom, Vim, Visual Studio Code and Git. The Mathematica Kernel also includes a command line front end. Other interfaces include JMath, based on GNU Readline and WolframScript which runs self-contained Mathematica programs (with arguments) from the UNIX command line.
Wolfram Mathematica : Capabilities for high-performance computing were extended with the introduction of packed arrays in version 4 (1999) and sparse matrices (version 5, 2003), and by adopting the GNU Multiple Precision Arithmetic Library to evaluate high-precision arithmetic. Version 5.2 (2005) added automatic multi-threading when computations are performed on multi-core computers. This release included CPU-specific optimized libraries. In addition Mathematica is supported by third party specialist acceleration hardware such as ClearSpeed. In 2002, gridMathematica was introduced to allow user level parallel programming on heterogeneous clusters and multiprocessor systems and in 2008 parallel computing technology was included in all Mathematica licenses including support for grid technology such as Windows HPC Server 2008, Microsoft Compute Cluster Server and Sun Grid. Support for CUDA and OpenCL GPU hardware was added in 2010.
Wolfram Mathematica : As of Version 14, there are 6,602 built-in functions and symbols in the Wolfram Language. Stephen Wolfram announced the launch of the Wolfram Function Repository in June 2019 as a way for the public Wolfram community to contribute functionality to the Wolfram Language. At the time of Stephen Wolfram's release announcement for Mathematica 13, there were 2,259 functions contributed as Resource Functions. In addition to the Wolfram Function Repository, there is a Wolfram Data Repository with computable data and the Wolfram Neural Net Repository for machine learning. Wolfram Mathematica is the basis of the Combinatorica package, which adds discrete mathematics functionality in combinatorics and graph theory to the program.
Wolfram Mathematica : Communication with other applications can be done using a protocol called Wolfram Symbolic Transfer Protocol (WSTP). It allows communication between the Wolfram Mathematica kernel and the front end and provides a general interface between the kernel and other applications. Wolfram Research freely distributes a developer kit for linking applications written in the programming language C to the Mathematica kernel through WSTP using J/Link., a Java program that can ask Mathematica to perform computations. Similar functionality is achieved with .NET /Link, but with .NET programs instead of Java programs. Other languages that connect to Mathematica include Haskell, AppleScript, Racket, Visual Basic, Python, and Clojure. Mathematica supports the generation and execution of Modelica models for systems modeling and connects with Wolfram System Modeler. Links are also available to many third-party software packages and APIs. Mathematica can also capture real-time data from a variety of sources and can read and write to public blockchains (Bitcoin, Ethereum, and ARK). It supports import and export of over 220 data, image, video, sound, computer-aided design (CAD), geographic information systems (GIS), document, and biomedical formats. In 2019, support was added for compiling Wolfram Language code to LLVM. Version 12.3 of the Wolfram Language added support for Arduino.
Wolfram Mathematica : Mathematica is also integrated with Wolfram Alpha, an online answer engine that provides additional data, some of which is kept updated in real time, for users who use Mathematica with an internet connection. Some of the data sets include astronomical, chemical, geopolitical, language, biomedical, airplane, and weather data, in addition to mathematical data (such as knots and polyhedra).
Wolfram Mathematica : BYTE in 1989 listed Mathematica as among the "Distinction" winners of the BYTE Awards, stating that it "is another breakthrough Macintosh application ... it could enable you to absorb the algebra and calculus that seemed impossible to comprehend from a textbook". Mathematica has been criticized for being closed source. Wolfram Research claims keeping Mathematica closed source is central to its business model and the continuity of the software.
Wolfram Mathematica : Official website Mathematica Documentation Center A little bit of Mathematica history documenting the growth of code base and number of functions over time
XGBoost : XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala. It works on Linux, Microsoft Windows, and macOS. From the project description, it aims to provide a "Scalable, Portable and Distributed Gradient Boosting (GBM, GBRT, GBDT) Library". It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask. XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions.
XGBoost : XG Boost initially started as a research project by Tianqi Chen as part of the Distributed (Deep) Machine Learning Community (DMLC) group. Initially, it began as a terminal application which could be configured using a libsvm configuration file. It became well known in the ML competition circles after its use in the winning solution of the Higgs Machine Learning Challenge. Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions. It was soon integrated with a number of other packages making it easier to use in their respective communities. It has now been integrated with scikit-learn for Python users and with the caret package for R users. It can also be integrated into Data Flow frameworks like Apache Spark, Apache Hadoop, and Apache Flink using the abstracted Rabit and XGBoost4J. XGBoost is also available on OpenCL for FPGAs. An efficient, scalable implementation of XGBoost has been published by Tianqi Chen and Carlos Guestrin. While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees. For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder.
XGBoost : Salient features of XGBoost which make it different from other gradient boosting algorithms include: Clever penalization of trees A proportional shrinking of leaf nodes Newton Boosting Extra randomization parameter Implementation on single, distributed systems and out-of-core computation Automatic feature selection Theoretically justified weighted quantile sketching for efficient computation Parallel tree structure boosting with sparsity Efficient cacheable block structure for decision tree training
XGBoost : XGBoost works as Newton–Raphson in function space unlike gradient boosting that works as gradient descent in function space, a second order Taylor approximation is used in the loss function to make the connection to Newton–Raphson method. A generic unregularized XGBoost algorithm is:
XGBoost : John Chambers Award (2016) High Energy Physics meets Machine Learning award (HEP meets ML) (2016)
XGBoost : LightGBM CatBoost == References ==
Yooreeka : Yooreeka is a library for data mining, machine learning, soft computing, and mathematical analysis. The project started with the code of the book "Algorithms of the Intelligent Web". Although the term "Web" prevailed in the title, in essence, the algorithms are valuable in any software application. It covers all major algorithms and provides many examples. Yooreeka 2.x is licensed under the Apache License rather than the somewhat more restrictive LGPL (which was the license of v1.x). The library is written 100% in the Java language.
Yooreeka : The following algorithms are covered: Clustering Hierarchical—Agglomerative (e.g. MST single link; ROCK) and Divisive Partitional (e.g. k-means) Classification Bayesian Decision trees Neural Networks Rule based (via Drools) Recommendations Collaborative filtering Content based Search PageRank DocRank Personalization
Yooreeka : Baynoo Website Yooreeka on GitHub Yooreeka on Google Code (old repository)
Conference on Computer Vision and Pattern Recognition : The Conference on Computer Vision and Pattern Recognition is an annual conference on computer vision and pattern recognition.
Conference on Computer Vision and Pattern Recognition : The conference was first held in 1983 in Washington, DC, organized by Takeo Kanade and Dana H. Ballard. From 1985 to 2010 it was sponsored by the IEEE Computer Society. In 2011 it was also co-sponsored by University of Colorado Colorado Springs. Since 2012 it has been co-sponsored by the IEEE Computer Society and the Computer Vision Foundation, which provides open access to the conference papers.
Conference on Computer Vision and Pattern Recognition : The conference considers a wide range of topics related to computer vision and pattern recognition—basically any topic that is extracting structures or answers from images or video or applying mathematical methods to data to extract or recognize patterns. Common topics include object recognition, image segmentation, motion estimation, 3D reconstruction, and deep learning. The conference generally has less than 30% acceptance rates for all papers and less than 5% for oral presentations. It is managed by a rotating group of volunteers who are chosen in a public election at the Pattern Analysis and Machine Intelligence-Technical Community (PAMI-TC) meeting four years before the meeting. The conference uses a multi-tier double-blind peer review process. The program chairs, who cannot submit papers, select area chairs who manage the reviewers for their subset of submissions.
Conference on Computer Vision and Pattern Recognition : The conference is usually held in June in North America.
Conference on Computer Vision and Pattern Recognition : International Conference on Computer Vision European Conference on Computer Vision