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About: Somoclu is a cluster-oriented implementation of self-organizing maps. It relies on MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Changes:Initial Announcement on mloss.org.
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About: A scalable, fast C++ machine learning library, with emphasis on usability. Changes:Speedups of cover tree traversers; addition of rank-approximate nearest neighbor (RANN); addition of fast exact max-kernel search (FastMKS); fix for EM covariance estimation; more parameters for GMM estimation; force GMM and GaussianDistribution covariance matrices to be positive definite during training; add a tolerance parameter to the Baum-Welch algorithm for HMM training; fix for compilation with clang; fix for k-furthest neighbor search.
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About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini. Changes:
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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL). Changes:
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About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs). Changes:Markov decision processes added (Kan & Shelton 2008) [ctmdp.h] Mean field inference added (Cohn, El-Hay, Friedman, & Kupferman 2009) [meanfieldinf.h] Factored uniformization for filtering added (Celikkaya & Shelton 2011) [uniformizedfactoredinf.h] Auxilliary Gibbs sampling added (Rao & Teh 2011) [gibbsauxsampler.h] Multi-threading for EM added many speed improvements unit testing improved [tst/] new demo "main" programs added [demo/] file format changed to XML-ish format (with old methods still there for conversion) matrix switched to Eigen package (with option to return to old matrix) glpk now included initial cmake functionality
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About: SVDFeature is a toolkit for developing generic collaborative filtering algorithms by defining features. Changes:JMLR MLOSS version.
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About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation. Changes:New in Version 0.9.6:
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About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar. Changes:This version introduces a number of new features:
Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package. This software has been tested on Linux, but it should run in other platforms with minor adaptations.
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About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16. Changes:VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme. VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).
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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).
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About: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines. Changes:Updated mloss entry (minor fixes).
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About: CARP: The Clustering Algorithms’ Referee Package Changes:Added generalized overlap, more metrics for comparing partitionings
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About: The Libra machine learning toolkit includes implementations of a variety of algorithms for learning and inference with Bayesian networks, Markov networks, dependency networks, and arithmetic circuits. Libra's strength is exploiting context-specific independence to allow exact inference in models with high treewidth. Changes:Version 0.4.0 (7/06/2011): * MF inference in DNs (mf -depnet) * Max-product algorithm for BNs and MNs (maxprod) * MPE inference in ACs (acquery -mpe) * Added support for UAI MN file format. * New fstats utility to get basic file statistics for most file types supported by Libra * And more!
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About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms. Changes:Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.
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About: The source code of the mldata.org site - a community portal for machine learning data sets. Changes:Initial Announcement on mloss.org.
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About: KReator is an integrated development environment (IDE) for relational probabilistic knowledge representation languages. At the moment, KReator supports Bayesian Logic Programs (BLPs), Markov Logic Networks (MLNs), Relational Maximum Entropy (RME), Relational Bayesian Networks (RBN), and Probabilistic Prolog (ProbLog). Changes:
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About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining. Changes:1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "--score-for-class C" causes scores to be computed relative to a given (positive) class. For two-class problems. Fixed bug in example sampling code (--sample n) Fixed bug keeping old-style example formats (terminated by dot) from working. More code restructuring.
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About: OpenViBE is an opensource platform that enables to design, test and use Brain-Computer Interfaces (BCI). Broadly speaking, OpenViBE can be used in many real-time Neuroscience applications [...] Changes:New release 0.8.0.
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About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive. Changes:Incremental update, fixing some cosmetic issues, coincides with JMLR publication.
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About: Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair. All you need is a collection of translated texts (parallel corpus). An efficient search algorithm finds quickly the highest probability translation among the exponential number of choices. Changes:Initial Announcement on mloss.org.
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