PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Oger: Modular Learning Architectures For Large-Scale Sequential Processing
David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers and Dejan Pecevski
Journal of Machine Learning Research Volume 13, Number 10, pp. 2995-2998, 2012.

Abstract

Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large datasets. It builds on MDP for its modularity, and adds processing of sequential datasets, several cross-validation schemes and parameter optimization methods. Additionally, several learning algorithms are implemented, such as different reservoir implementations (both sigmoid and spiking), ridge regression, Conditional Restricted Boltzmann Machine (CRBM) and others. Oger is released under the GNU LGPL, and is available from http://organic.elis.ugent.be/oger.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9031
Deposited By:Wolfgang Maass
Deposited On:21 February 2012