About: PLEASD: A Matlab Toolbox for Structured Learning Changes:Initial Announcement on mloss.org.

About: A general purpose library to process and predict sequences of elements using echo state networks. Changes:Initial Announcement on mloss.org.

About: Mulan is an opensource Java library for learning from multilabel datasets. Multilabel datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multilabel dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. Changes:Learners
Measures/Evaluation
Bug fixes
API changes
Miscellaneous

About: MLwizard recommends and optimizes classification algorithms based on metalearning and is a software wizard fully integrated into RapidMiner but can be used as library as well. Changes:Faster parameter optimization using genetic algorithm with predefined start population.

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels. Changes:Added missing dataset function (thanks to Hao Wu)

About: This package contains a python and a matlab implementation of the most widely used algorithms for multiarmed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies. Changes:Initial Announcement on mloss.org.

About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are underdetermined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping. Changes:Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.

About: Fast C++ implementation of the variation of information (Meila 2003) and Rand index (Rand 1971) with MATLAB mex files Changes:Initial Announcement on mloss.org.

About: Motivated by a need to classify highdimensional, heterogeneous data from the bioinformatics domain, we developed MLFlex, a machinelearning toolbox that enables users to perform twoclass and multiclass classiﬁcation analyses in a systematic yet ﬂexible manner. MLFlex was written in Java but is capable of interfacing with thirdparty packages written in other programming languages. It can handle multiple inputdata formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, MLFlex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.) 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: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "LpLq Sparse Linear and Sparse Multiple Kernel MultiTask Learning". Changes:Initial Announcement on mloss.org.

About: 3layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM. Changes:Initial Announcement on mloss.org.

About: Matlab SVM toolbox for learning large margin filters in signal or images. Changes:Initial Announcement on mloss.org.

About: The SSA Toolbox is an efficient, platformindependent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of nonstationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex highdimensional data sets. Changes:

About: Learns gradient boosted regression tree ensembles in parallel on shared memory or cluster systems Changes:Initial Announcement on mloss.org.

About: MLPlot is a lightweight plotting library written in Java. Changes:Initial Announcement on mloss.org.

About: Multiclass vector classification based on cost functiondriven learning vector quantization , minimizing misclassification. Changes:Initial Announcement on mloss.org.

About: Fast RuntimeFlexible Multidimensional Arrays and Views for C++ Changes:Initial Announcement on mloss.org.

About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, labelspecific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving autoassociation problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful lowdimensional mappings. Changes:Tue Jul 5 14:40:03 CEST 2011  Bugfixes and cleanups

About: GridSoccer Simulator is a multiagent soccer simulator in a gridworld environment. The environment provides a testbed for machinelearning, and control algorithms, especially multiagent reinforcement learning. Changes:Initial Announcement on mloss.org.
