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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: 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: A fast implementation of several stochastic gradient descent learners for classification, ranking, and ROC area optimization, suitable for large, sparse data sets. Includes Pegasos SVM, SGD-SVM, Passive-Aggressive Perceptron, Perceptron with Margins, Logistic Regression, and ROMMA. Commandline utility and API libraries are provided. Changes:Initial Announcement on mloss.org.
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About: A (randomized) coordinate descent procedure to minimize L1 regularized loss for classification and regression purposes. Changes:Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.
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About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Changes:
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About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques. Changes:
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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...] Changes:This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer. Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic). Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions. Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures). Unified automatic input checking via new static typing extending Python properties. Full support for recursive composition of larger components containing arbitrary statically typed state variables.
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About: RL-Glue allows agents, environments, and experiments written in Java, C/C++, Matlab, Python, and Lisp to inter operate, accelerating research by promoting software re-use in the community. Changes:RL-Glue paper has been published in JMLR.
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About: This package implements the “Online Random Forests” (ORF) algorithm of Saffari et al., ICCV-OLCV 2009. This algorithm extends the offline Random Forests (RF) to learn from online training data samples. ORF is a multi-class classifier which is able to learn the classifier without 1-vs-all or 1-vs-1 binary decompositions. Changes:Initial Announcement on mloss.org.
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About: EANT Without Structural Optimization is used to learn a policy in either complete or partially observable reinforcement learning domains of continuous state and action space. Changes:Initial Announcement on mloss.org.
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About: Source code for EM approximate learning in the Latent Topic Hypertext Model. Changes:Initial Announcement on mloss.org.
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About: This software implements the Dirichlet Forest (DF) Prior within the Latent Dirichlet Allocation (LDA) model. When combined with LDA, the Dirichlet Forest Prior allows the user to encode domain knowledge (must-links and cannot-links between words) into the prior on topic-word multinomials. Changes:Initial Announcement on mloss.org.
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About: The Computational Infrastructure for Operations Research (COIN-OR) project is an initiative to spur the development of open-source software for the operations research community. Changes:Initial Announcement on mloss.org.
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About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs. Changes:small bug fix (thx nicolas ;)
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About: OLaRankGreedy is an online solver of the dual formulation of support vector machines for sequence labeling using greedy inference. Changes:Initial Announcement on mloss.org.
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About: OLaRank is an online solver of the dual formulation of support vector machines for sequence labeling using viterbi decoding. Changes:Initial Announcement on mloss.org.
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About: Preparing Changes:Initial Announcement on mloss.org.
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About: OLL is a library supporting several for online-learning algorithms, which provides C++ library, and stand-alone programs for learning, predicting. OLL is specialized for large-scale, but sparse, [...] Changes:Initial Announcement on mloss.org.
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About: Ohmm is a library for learning hidden Markov models by using Online EM algorithm. This library is specialized for large scale data; e.g. 1 million words. The output includes parameters, and estimation results. Changes:Initial Announcement on mloss.org.
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