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: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. Changes:New features:
Fix:

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 1.2.4

About: The KernelMachine 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).

About: Python module to ease pattern classification analyses of large datasets. It provides highlevel abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...] Changes:
This release aggregates all the changes occurred between official
releases in 0.4 series and various snapshot releases (in 0.5 and 0.6
series). To get better overview of high level changes see
:ref:
Also adapts changes from 0.4.6 and 0.4.7 (see corresponding changelogs).
This is a special release, because it has never seen the general public.
A summary of fundamental changes introduced in this development version
can be seen in the :ref: Most notably, this version was to first to come with a comprehensive twoday workshop/tutorial.
A bugfix release
A bugfix release

About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions. Changes:Now supports OLS and GLS regression and NaiveBayes classification

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.

About: Cubist is the regression counterpart to the C5.0 decision tree tool. Changes:Initial Announcement on mloss.org.

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.

About: PSVM  Support vector classification, regression and feature extraction for nonsquare dyadic data, nonMercer kernels. Changes:Initial Announcement on mloss.org.

About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included. Changes:Spectrum LSTM package included

About: LIBSVM is an integrated software for support vector classification, (CSVC, nuSVC ), regression (epsilonSVR, nuSVR) and distribution estimation (oneclass SVM). It supports multiclass [...] Changes:Initial Announcement on mloss.org.

About: Matlab code for semisupervised regression and dimensionality reduction using Hessian energy. Changes:Initial Announcement on mloss.org.

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.

About: BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem. Changes:Initial Announcement on mloss.org.

About: This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares. Changes:

About: The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. Changes:Initial Announcement on mloss.org.

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features. Changes:Initial Announcement on mloss.org.

About: Nieme is a C++ machine learning library for largescale classification, regression and ranking. It provides a simple interface available in C++, Python and Java and a user interface for visualization. Changes:Released Nieme 1.0

About: BenchMarking Via Weka is a clientserver architecture that supports interoperability between different machine learning systems. Machine learning systems need to provide mechanisms for processing [...] Changes:Initial Announcement on mloss.org.
