About: BCPy2000 provides a platform for rapid, flexible development of experimental BrainComputer 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 lessdocumented, optional tools)

About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.

About: libnabo is a fast K Nearset Neighbor library for lowdimensional spaces. Changes:

About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search. Changes:See project page for changes.

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing. Changes:CHANGES IN VERSION 2.8.0NEW FEATURES
CHANGES IN VERSION 2.4.0
CHANGES IN VERSION 2.3.1NEW FEATURES
2.0.0:
1.4.0:

About: Graphical user interface for data mining in R Changes:Fetched by rcranrobot on 20130401 00:00:07.700426

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a userfriendly open source software in MATLAB and Octave and is freely available on the website. Changes:New in toolbox

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 datadependent [...] Changes:Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser  ferdinand.kaiser@tut.fi)

About: OpenViBE is an opensource platform that enables to design, test and use BrainComputer Interfaces (BCI). Broadly speaking, OpenViBE can be used in many realtime Neuroscience applications [...] Changes:New release 0.8.0.

About: Orange is a componentbased 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:The core of the system (except the GUI) no longer includes any GPL code and can be licensed under the terms of BSD upon request. The graphical part remains under GPL. Changed the BibTeX reference to the paper recently published in JMLR MLOSS.

About: Generalized linear and additive models by likelihood based boosting Changes:Fetched by rcranrobot on 20130401 00:00:04.893311

About: KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench into the opensource scientific workflow Kepler. Among them are classification, [...] Changes:

About: Aika is an open source text mining engine. It can automatically extract and annotate semantic information in text. In case this information is ambiguous, Aika will generate several hypothetical interpretations about the meaning of this text and retrieve the most likely one. Changes:Aika Version 0.17 20180514
Aika Version 0.15 20180316

About: VR Changes:Fetched by rcranrobot on 20091003 07:16:05.643423

About: A fast and scalable graphbased clustering algorithm based on the eigenvectors of the nonlinear 1Laplacian. Changes:

About: A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and a tree data structure. This library is useful for efficient Kernel Density [...] Changes:Initial Announcement on mloss.org.

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software. Changes:Initial Announcement on mloss.org.

About: An open source Java software providing collaborative filtering algorithms. Changes:Initial Announcement on mloss.org.

About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. Changes:Initial Announcement on mloss.org.

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, NadarayaWatson estimator); (3) generative models for random networks (smallworld, scalefree, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition. Changes:JProGraM 13.2  CHANGE LOG Release date: February 13, 2012 New features:  Support for Fiedler random graphs/random field models for largescale networks (ninofreno.graph.fiedler package);  Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).
