%0 Journal Article
%T Error-Correcting Output Codes Library
%A Sergio Escalera
%A Oriol Pujol
%A Petia Radeva
%J Journal of Machine Learning Research
%D 2010
%V 11
%@ 1532-4435
%F Sergio Escalera2010
%O MILAB;HUPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1286), last updated on Thu, 18 Jan 2018 12:01:57 +0100
%X (Feb):661−664In 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, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.
%U http://dl.acm.org/citation.cfm?id=1756026
%P 661-664