KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench [1] into the open-source scientific workflow Kepler [2]. Among them are classification, [...]

BenchMarking Via Weka is a client-server architecture that supports interoperability between different machine learning systems. Machine learning systems need to provide mechanisms for processing [...]

The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Breiman and Cutler's random forests for classification and regression: Classification and regression based on a forest of trees using random inputs.

libDAI provides FOSS implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Experiment Databases for Machine Learning is a large public database of machine learning experiments as well as a framework for producing similar databases for specific goals. It provides a way to [...]

L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model: A package for fitting possibly high dimensional penalized regression models. The penalty structure can be any combination [...]

The SGD package contains a stochastic gradient implementation of linear SVMs and linear CRFs. It demonstrate that a simple stochastic gradient descent is very competitive algorithm for such tasks. [...]

binsdfc is a command line implementation of the algorithm described in [Endres,Oram,Schindelin,Foldiak:Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms, [...]

Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists. The [...]

L1 constrained estimation aka `lasso': Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998)

Ishwaran and Kogalur's Random Survival Forest: Ensemble survival analysis based on a random forest of trees using random inputs.

VR: Functions and datasets to support Venables and Ripley, 'Modern Applied Statistics with S' (4th edition).

Nested Effects Models (NEMs) are a class of directed graphical models originally introduced to analyze the effects of gene perturbation screens with high-dimensional phenotypes. In contrast to other [...]

Variable selection using random forests: Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection [...]

Classification rule based on Bayesian naive Bayes models with feature selection bias corrected: This software is used to predict the binary response based on high dimensional features, for example [...]

Bayesian Prediction with High-order Interactions: This software can be used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The [...]

The goal of the project is to provide a programming environment for easily exploring advanced topics in artificial intelligence and robotics without having to worry about the low-level details of [...]

The High-Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifier for high-dimensional data. This classifier is based on a mixture of Gaussian models adapted for [...]

The High Dimensional Discriminant Analysis (HDDA) toolbox contains an efficient supervised classifier for high-dimensional data. This classifier is based on Gaussian models adapted for [...]