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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 ... |
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-mul... |
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 librari... |
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 lear... |
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 solut... |
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 provid... |
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 re... |
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' appe... |
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 Lice... |
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#. Sho... |
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, K... |
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 arbitr... |
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 Researc... |
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 co... |
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.... |
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 201... |
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 ou... |
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 a... |
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 Mod... |
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 rese... |
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: Acade... |
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 MLConte... |
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 pr... |
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 remove... |
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 Arch... |
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 s... |
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 constructio... |
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... |
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 I... |
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 (Py... |
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 unstructur... |
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 f... |
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 i... |
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... |
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 th... |
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 general... |
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 in... |
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 includin... |
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... |
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 Murm... |
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, th... |
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 fa... |
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 ... |
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 Lear... |
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 i... |
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 ... |
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 SIGKD... |
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 KN... |
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, impl... |
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... |
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-... |
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 Wolfr... |
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 ... |
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,... |
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 fo... |
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 Distrib... |
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... |
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 sel... |
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... |
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 Persona... |
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 ... |
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 t... |
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 |
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