Em em is a package which enables to create Gaussian Mixture Models (diagonal and full covariance matrices supported), to sample them, and to estimate them from data using Expectation Maximization algorithm. It can also draw confidence ellipsoides for multivariate models, and compute the Bayesian Information Criterion to assess the number of clusters in the data. In a near future, I hope to add so-called online EM (ie recursive EM) and variational Bayes implementation. em is implemented in python, and uses the excellent numpy and scipy packages. The toolbox depends on several packages to work: numpyscipysetuptoolsmatplotlib (if you wish to use the plotting facilities: this is not mandatory) Those packages are likely to be already installed in a typical numpy/scipy environment. Since July 2007, the toolbox is included in the learn scikits (scikits). svn co scikits.dev python setup.py install You can (and should) also test em installation using the following:
Home Page of Thorsten Joachims · International Conference on Machine Learning (ICML), Program Chair (with Johannes Fuernkranz), 2010. · Journal of Machine Learning Research (JMLR) (action editor, 2004 - 2009). · Machine Learning Journal (MLJ) (action editor). · Journal of Artificial Intelligence Research (JAIR) (advisory board member). · Data Mining and Knowledge Discovery Journal (DMKD) (action editor, 2005 - 2008). · Special Issue on Learning to Rank for IR, Information Retrieval Journal, Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, T. · Special Issue on Automated Text Categorization, Journal on Intelligent Information Systems, T. · Special Issue on Text-Mining, Zeitschrift Künstliche Intelligenz, Vol. 2, 2002. · Enriching Information Retrieval, P. · Redundancy, Diversity, and Interdependent Document Relevance (IDR), P. · Beyond Binary Relevance, P. · Machine Learning for Web Search, D. · Learning to Rank for Information Retrieval, T. · Learning in Structured Output Spaces, U. · Learning for Text Categorization.
LIBLINEAR -- A Library for Large Linear Classification Machine Learning Group at National Taiwan University Contributors We recently released LibShortText, a library for short-text classification and analysis. It's built upon LIBLINEAR. Version 1.94 released on November 12, 2013. Following the recent change of LIBSVM, we slightly adjust the way class labels are handled internally. By default labels are ordered by their first occurrence in the training set. An experimental version using 64-bit int is in LIBSVM tools. We are interested in large sparse regression data. A practical guide to LIBLINEAR is now available in the end of LIBLINEAR paper. Some extensions of LIBLINEAR are at LIBSVM Tools. LIBLINEAR is the winner of ICML 2008 large-scale learning challenge (linear SVM track). Introduction LIBLINEAR is a linear classifier for data with millions of instances and features. Main features of LIBLINEAR include FAQ is here When to use LIBLINEAR but not LIBSVM Download LIBLINEAR The package includes the source code in C/C++. R. Interfaces to LIBLINEAR
LIBSVM Data: Classification (Binary Class) This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. Many are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. a1a Source: UCI / AdultPreprocessing: The original Adult data set has 14 features, among which six are continuous and eight are categorical. a2a Source: UCI / AdultPreprocessing: The same as a1a. a3a Source: UCI / AdultPreprocessing: The same as a1a. a4a Source: UCI / AdultPreprocessing: The same as a1a. a5a Source: UCI / AdultPreprocessing: The same as a1a. a6a Source: UCI / AdultPreprocessing: The same as a1a. a7a Source: UCI / AdultPreprocessing: The same as a1a. a8a Source: UCI / AdultPreprocessing: The same as a1a. a9a Source: UCI / AdultPreprocessing: The same as a1a. australian Source: Statlog / Australian# of classes: 2# of data: 690# of features: 14Files: breast-cancer cod-rna colon-cancer covtype.binary diabetes duke breast-cancer heart
Sphinx-4 - A speech recognizer written entirely in the Java(TM) programming language Overview Sphinx4 is a pure Java speech recognition library. It provides a quick and easy API to convert the speech recordings into text with the help CMUSphinx acoustic models. It can be used on servers and in desktop applications. Sphinx4 supports US English and many other languages. Using in your projects As any library in Java all you need to do to use sphinx4 is to add jars into dependencies of your project and then you can write code using the API. The easiest way to use modern sphinx4 is to use modern build tools like Apache Maven or Gradle. <project> ... Then add sphinx4-core to the project dependencies: <dependency><groupId>edu.cmu.sphinx</groupId><artifactId>sphinx4-core</artifactId><version>5prealpha-SNAPSHOT</version></dependency> Add sphinx4-data to dependencies as well if you want to use default acoustic and language models: <dependency><groupId>edu.cmu.sphinx</groupId><artifactId>sphinx4-data</artifactId><version>5prealpha-SNAPSHOT</version></dependency> Basic Usage or Demos
Em em is a package which enables to create Gaussian Mixture Models (diagonal and full covariance matrices supported), to sample them, and to estimate them from data using Expectation Maximization algorithm. It can also draw confidence ellipsoides for multivariate models, and compute the Bayesian Information Criterion to assess the number of clusters in the data. In a near future, I hope to add so-called online EM (ie recursive EM) and variational Bayes implementation. em is implemented in python, and uses the excellent numpy and scipy packages. Numpy is a python packages which gives python a fast multi-dimensional array capabilities (ala matlab and the likes); scipy leverages numpy to build common scientific features for signal processing, linear algebra, statistics, etc... The toolbox depends on several packages to work: numpyscipysetuptoolsmatplotlib (if you wish to use the plotting facilities: this is not mandatory) Since July 2007, the toolbox is included in the learn scikits (scikits).
Database Mining Tutorial What's Database Text Mining? This tutorial shows how to use a relational database management system (RDBMS) to store documents and LingPipe analyses. It uses MEDLINE data as the example data, and MySQL as the example RDBMS. For expository purposes, we break this task into three parts: Loading MEDLINE data into the database, using the LingMed MEDLINE parser and the JDBC API to access a RDBMS.Using the LingPipe API to annotate text data in the database, and to store the annotations back into the database.SQL database queries over the annotated data. Completing part 1 results in a simple database containing a table containing the titles and abstracts of MEDLINE citations. MySQL MySQL runs on most operating systems, including Linux, Unix, Windows, and MacOS, and is available under both a commercial and GPL license. MySQL 5.0 Download Page. You will only need the "essentials" version for Windows x86 or AMD64. The official JDBC driver for MySQL is available from the Creating the database
Torch3: The Dream Comes True Iris Data Set Source: Creator: R.A. Fisher Donor: Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov) Data Set Information: This is perhaps the best known database to be found in the pattern recognition literature. Predicted attribute: class of iris plant. This is an exceedingly simple domain. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). Attribute Information: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica Relevant Papers: Fisher,R.A. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". See also: 1988 MLC Proceedings, 54-64. Papers That Cite This Data Set1: Ping Zhong and Masao Fukushima. Sotiris B. .
Nike Running pcSVM Package Index > pcSVM > pre 1.0 Not Logged In pcSVM pre 1.0 pcSVM is a framework for support vector machines pcSVM is a framwork for support vector machines. Support Vector Machines is a new generation of learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition. Downloads (All Versions): 0 downloads in the last day 0 downloads in the last week 0 downloads in the last month Website maintained by the Python community Real-time CDN by Fastly / hosting by Rackspace / design by Tim Parkin Online Access The DBpedia data set can be accessed online via a SPARQL query endpoint and as Linked Data. 1. Querying DBpedia The DBpedia data set enables quite astonishing query answering possibilities against Wikipedia data. 1.1. Public SPARQL Endpoint There is a public SPARQL endpoint over the DBpedia data set at OpenLink Virtuoso as the back-end database engine. There is a list of all DBpedia data sets that are currently loaded into the SPARQL endpoint. You can ask queries against DBpedia using: the Leipzig query builder at the OpenLink Interactive SPARQL Query Builder (iSPARQL) at the SNORQL query explorer at (does not work with Internet Explorer); or any other SPARQL-aware client(s). Fair Use Policy: Please read this post for information about restrictions on the public DBpedia endpoint. 1.2. There is a public Faceted Browser “search and find” user interface at 1.3. here. 1.4. 1.5. 1.6.
guide.pdf Multiclass Support Vector Machine | GPU Computing