Elefant For Mac

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) includes modules for many common optimisation problems arising in machine learning and inference.

Elefant is designed to be modular and easy to use. Framework provides easy to use Python interface, which can be use for quick prototyping and testing inference algorithms.

Following machine learning algorithms are implemented in the Elefant: · Support Vector Machine (SVM) for classification, regression, quantile and novelty detection, online learning, Epsilon Insensitive and Laplacian support vector regression. · Heteroscedastic Gaussian Process regression, Gaussian Process Regression, Multi-class transductive classification with Gaussian Process. · Solvers for the quadratic programming problem · BAHSIC feature selection · Algorithms for fast computation and manipulation of kernel matrices. Linear, RBF, Dot Product and String Kernels · Loopy Belief Propagation and Junction Tree algorithms. · Cover Tree for calculating the nearest neighbor

Elefant is currently being tested on the following platforms: · Mac OS X (10.4) · Linux (Ubuntu 7.04 - Feisty Fawn) · Windows XP (SP2), Windows Vista