About: STK++: A Statistical Toolkit Framework in C++ Changes:Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix

About: LogRegCrowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979). Changes:Initial Announcement on mloss.org.

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over datadependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. Changes:improved testing, improved documentation, windows compatibility, more algorithms

About: revised version of BACOM Changes:Initial Announcement on mloss.org.

About: Scriptfriendly commandline tools for machine learning and data mining tasks. (The commandline tools wrap functionality from a public domain C++ class library.) Changes:Added support for CUDA GPUparallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms. Changes:Changes for Encog 3.2: Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing  TestHessian Issue #46: Couple of small fixes  Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing  Matrix not full rank Issue #42: Nuget  NuSpec Issue #36: Load Examples easier

About: MultiBoost is a multipurpose boosting package implemented in C++. It is based on the multiclass/multitask AdaBoost.MH algorithm [SchapireSinger, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Major changes :
Minor fixes:

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semisupervised tasks. Changes:The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:
The API and ABI have undergone significant changes, many of which are due to the transition to C++11.

About: Loglinear analysis for highdimensional data Changes:Initial Announcement on mloss.org.

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications. Changes:

About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.

About: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.

About: ELKI is a framework for implementing datamining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods. Changes:Additions and Improvements from ELKI 0.5.5: Algorithms Clustering:
Outlier detection
Distances
Database Layer and Data Types Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and pstable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogrambased jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA) Index Layer
Mathematics and Statistics
Visualization
Other

About: The Gesture Recognition Toolkit (GRT) is a crossplatform, opensource, c++ machine learning library that has been specifically designed for realtime gesture recognition. It features a large number of machinelearning algorithms for both classification and regression in addition to a wide range of supporting algorithms for preprocessing, feature extraction and dataset management. The GRT has been designed for realtime gesture recognition, but it can also be applied to more general machinelearning tasks. Changes:Added Decision Tree and Random Forests.

About: DDN learns and visualize differential dependency networks from conditionspecific data. Changes:Initial Announcement on mloss.org.

About: The CAM RJava software provides a noval way to solve blind source separation problem. Changes:In this version, we fix the problem of not working under newest R version R3.0.

About: MyMediaLite is a lightweight, multipurpose library of recommender system algorithms. Changes:Mostly bug fixes. For details see: https://github.com/zenogantner/MyMediaLite/blob/master/doc/Changes

About: The mission of this project is to build and support a community interested in machine learning and machine intelligence based on modeling the neocortex and the principles upon which it works. Changes:Initial Announcement on mloss.org.

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Changes:

About: MLDemos is a userfriendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning. Changes:New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with nonnumerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bugfixes for display, import/export of data, classification performance New Algorithms and methodologies Added Projections to preprocess data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added GridSearch panel for batchtesting ranges of values for up to two parameters at a time Added OnevsAll multiclass classification for nonmulticlass algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)
