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: 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: Lasso and elasticnet regularized generalized linear models Changes:Fetched by rcranrobot on 20130401 00:00:05.081872

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/lowrank learning/reconstruction based on the dual augmented Lagrangian method. Changes:

About: Automatic Analysis of Malware Behavior using Machine Learning Changes:Support for new version of libarchive. Minor bug fixes.

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sumproduct networks, arithmetic circuits, and mixtures of trees. Changes:Version 1.1.2c (6/24/2015):

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinearoptimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:Fixed bugs and doc typos

About: Classification and visualization Changes:Fetched by rcranrobot on 20130401 00:00:05.722314

About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:

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 0.6.0:
Clustering algorithms: Kmeans
CLARA clustering Xmeans Hierarchical clustering
LSDBC clustering EM clustering was refactored and moved into its own package. The new version is much more extensible. Parallel computation framework, and some parallelized algorithms
Input:
Classification:
Evaluation: Internal cluster evaluation:
Statistical dependence measures:
Distance functions:
Preprocessing:
Indexing improvements:
Frequent Itemset Mining:
Uncertain clustering:
Outlier detection changes / smaller improvements:
Various:
