MiLDe is an integrated development environment with a suite of machine learning tools including SVMs, Neural Networks (using the Torch package), image analysis, numeric functions and dataset handling.

Torch 5 provides a matlab-like environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to a easy and fast scripting language (Lua) and a underlying C implementation.

Lush is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications. Lush is designed to be used in situations where one would want to combine the flexibility of a high-level, weakly-typed interpreted language, with the efficiency of a strongly-typed, natively-compiled language, and with the easy integration of code written in C, C++, or other languages. Lush is Free Software (under the GPL license) and runs on GNU/Linux, Solaris, Irix, and Windows under Cygwin.

Matlab Toolbox for Kernel Methods: The Spider. We are designing and developing a matlab toolbox for kernel methods. The goals of this project are: to build a general purpose kernel methods library including different induction principles such as (but not limited to) online/batch learning, active learning, etc., and to build a platform with different datasets and a code repository where researchers could exchange results and reproduce experiments.The spider is intended to be a complete object orientated environment for machine learning in Matlab. ( Project home page )

UniverSVM : A SVM Implementation for Large Scale Transduction and Inference with a Universum The UniverSVM is a SVM implementation written in C++. Its functionality comprises large scale transduction (as described in Large Scale Transductive SVMs), sparse solutions (as described in Trading Convexity for Scalability) and inference with a universum (as described in Inference with the Universum).

LaSVM is an online SVM algorithm based on a single pass through the data. LASVM yields competitive misclassification rates after a single pass over the training examples, outspeeding state-of-the-art SVM solvers. It can also use active example selection to yield faster training, higher accuracies, and simpler models, using only a fraction of the training example labels. Main Project Page

SENNA is a fast neural-network architecture for semantic extraction from text.

SVM-FOLD is a web server that makes predictions of family, superfamily and fold level classifications of proteins based on the Structural Classification of Proteins (SCOP) hierarchy using the SVM learning algorithm.

RankProp is a ranking algorithm that exploits the entire network structure of similarity relationships among proteins in a sequence database by performing a diffusion operation on a pre-computed, weighted network. The resulting ranking algorithm, evaluated using a human-curated database of protein structures, is efficient and provides significantly better rankings than a local network search algorithm such as PSI-BLAST.