A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

R/Weka interface: An R interface to Weka (Version 3.6.0). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, [...]

The Hidden Topic Markov Model

The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL. It extends Inductive Logic Programming to Description Logics and the [...]

Lasso and elastic-net regularized generalized linear models: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and [...]

The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and [...]

Generalized linear and additive models by likelihood based boosting: This package provides routines for fitting generalized linear and and generalized additive models by likelihood based boosting, [...]

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to [...]