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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models. Changes:We now support inference on large datasets using the FITC approximation for non-Gaussian likelihoods for EP and Laplace's approximation. New likelihood functions: mixture likelihood, Poisson likelihood, label noise. We added two MCMC samplers.
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About: GradMC is an algorithm for MR motion artifact removal implemented in Matlab Changes:Initial Announcement on mloss.org.
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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: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching. Changes:Initial Announcement on mloss.org.
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About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:contributed by George Papandreou:
gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg) slight modification to mat/@matFFTN/mvm.m to make it more consistent simple gradient solver using Barzilai-Borwein step size pls/plsBB.m
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About: The software provides an implementation of a filter/smoother based on Gibbs sampling, which can be used for inference in dynamical systems. Changes:Initial Announcement on mloss.org.
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About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org
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About: Orthonormal wavelet transform for D dimensional tensors in L levels. Generic quadrature mirror filters and tensor sizes. Runtime is O(n), plain C, MEX-wrapper and demo provided. Changes:Initial Announcement on mloss.org. |
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: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...] Changes:
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About: dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and [...] Changes:Initial Announcement on mloss.org.
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About: The Delay vector variance (DVV) method uses predictability of the signal in phase space to characterize the time series. Using the surrogate data methodology, so called DVV plots and DVV scatter [...] Changes:Initial Announcement on mloss.org.
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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM Changes:Initial Announcement on mloss.org.
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About: This is an implementation of variational Dirichlet process Gaussian mixtures. Thus, this works like the k-means, but it searched for the number of clusters as well. Couple algorithms are [...] Changes:Initial Announcement on mloss.org.
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About: BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods Changes:Initial Announcement on mloss.org.
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About: Code for automatically selecting the kernel parameters of an SVM. It is based on a gradient descent minimization of either the radius/margin bound, the leave-one-out error, a validation error or the [...] Changes:Initial Announcement on mloss.org.
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About: PLearn is a large C++ machine-learning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...] Changes:Initial Announcement on mloss.org.
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About: Very simple code for training SVMs in the primal. Works particularly well on sparse linear problems. In the non-linear case the entire kernel matrix needs to be computed, so for large problems it is [...] Changes:Initial Announcement on mloss.org.
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About: Torch is a statistical machine learning library written in C++ at IDIAP, Changes:Initial Announcement on mloss.org.
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