Error-Correcting Ouput Codes Library

In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, -density, attenuated, loss-based, probabilistic kernel-based, and lossweighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Sergio Escalera, Oriol Pujol, Petia Radeva
• Keywords: Dense Random, Inverse Hamming, JMLR 2010, Multi-class Categorization Problems