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About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...] Changes:Update for v2.0
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About: Machine Learning PYthon (mlpy) is a high-performance Python package for predictive modeling. Changes:New features:
Several bugs fixed
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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...] Changes:Initial Announcement on mloss.org.
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About: BCPy2000 provides a platform for rapid, flexible development of experimental Brain-Computer Interface systems based on the BCI2000.org project. From the developer's point of view, the implementation [...] Changes:Bugfixes and tuneups, and an expanded set of (some more-, some less-documented, optional tools)
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About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from large-scale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.
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About: Toolbox for circular statistics with Matlab (The Mathworks). Changes:Some bugfixes.
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About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents. Changes:
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About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports learning and inference for various probabilistic graphical models, along with parametric, semiparametric, and nonparametric density estimation. Changes:JProGraM 10.5 -- CHANGE LOG Release date: May 30, 2010 -- Support for continuous graphical models has been added: Gaussian, nonparanormal, and kernel-based Markov random fields, hybrid random fields and Bayes nets are now implemented; -- Routines for kernel-based conditional density estimation (Nadaraya-Watson estimators) have been implemented, with support for scalable dual-tree recursion techniques (used in the bandwidth selection routines); -- Methods for generating arbitrarily shaped multivariate density functions and for sampling datasets from them have been added; -- Independent Component Analysis is now also supported (by wrapping the FastICA library); -- Gaussian mixture models have been greatly improved by adding support for expectation-maximization, fixing some numerical stability issues and differentiating a simpler version with diagonal covariance matrices from a more complex version with full covariance matrices; -- Other minor features have been added, and a number of corrections have been introduced. Note that the Gaussian and nonparanormal Markov random fields relying on the graphical lasso technique require a working R distribution (http://www.r-project.org/) to be installed on your system, including in particular the external glasso package (http://www-stat.stanford.edu/~tibs/glasso/). Provided that R and the glasso package are correctly installed, the relevant JProGraM routines are able to exploit the R installation without any manual intervention. This means that users of the JProGraM library can sinmply call the routines executing the graphical lasso within their Java code without the need to manipulate any R code. (This has been tested successfully on several Linux distributions, but not on Windows or Mac OS X). JProGraM 9.1 -- CHANGE LOG Release date: January 29, 2009 -- Principal Components Analysis is now supported; -- A number of bugs within the ninofreno.gmm and ninofreno.clustering packages have been fixed; -- Other minor features have been added (especially within the MyMath class). JProGraM 8.10 -- CHANGE LOG Release date: October 7, 2008 The following algorithms are now supported by JProGraM: -- K-Means (for clustering); -- Kaufman-Rousseuw algorithm for initializing cluster centroids; -- Gaussian Mixture Model for probability density function estimation. JProGraM 8.6 -- CHANGE LOG Release date: June 8, 2008 The following statistical models are now supported by JProGraM: -- Parzen Windows for probability density function estimation; -- Probabilistic decision trees for discrete pattern classification; -- Dependency networks for discrete pseudo-likelihood estimation. JProGraM 8.1 Release date: February 16, 2008 The following statistical models are supported by JProGraM: -- Bayesian networks; -- Markov random fields; -- Hybrid random fields.
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About: Python Machine Learning Toolkit Changes:Cleaned up and tested code. Removed some dependencies. Better documentation. Changed the classification interface to separate model learning from model usage.
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About: Modular toolkit for Data Processing (MDP) is a library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. Changes:
The new additions in the library have been thoroughly tested but, as usual after a public release, we especially welcome user's feedback and bug reports.
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About: dANN is an Artificial Intelligence and Artificial Genetics library targeted at employing conventional techniques as well as acting as a platform for research & development of novel techniques. As new techniques are developed and proven to be effective they will be integrated into the core library. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it directly from GIT. Changes:Please get the version in GIT only, the released version is old.
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About: ALGLIB is an open source numerical analysis library distributed under GPL 2+. It implements both general numerical algorithms and machine learning algorithms. ALGLIB can be used from C#, C++, FreePascal, VBA and other languages. It is the only numerical analysis library which uses automatic translation to generate source code written in different programming languages with 100% identical functionality. Changes:
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About: PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined [...] Changes:
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About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...] Changes:Initial Announcement on mloss.org.
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About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc. Changes:Initial Announcement on mloss.org.
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About: Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...] Changes:0.4.4 (Mon, Feb 2 2010) (Total: 144 commits) Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward.
o GNB implements Gaussian Naïve Bayes Classifier. o read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack). o SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation). o New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing. o Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).
o To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance. o Adopted copy.py from python2.6 (support Ellipsis as well). ed (38 BF commits): o GLM output does not depend on the enabled states any more. o Variety of docstrings fixed and/or improved. o Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training). o sg : + KRR is optional now – avoids crashing if KRR is not available.
o Python 2.4 compatibility issues: kNN and IFS
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About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...] Changes:Initial Announcement on mloss.org.
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About: A Java library to create, process and manage mixtures of exponential families. Changes:Initial Announcement on mloss.org.
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About: MLPACK is the first comprehensive scalable machine learning library. Changes:Initial Announcement on mloss.org.
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About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...] Changes:Version 1.2.3
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