TY  - JOUR
AU  - Sergio Escalera
AU  - Oriol Pujol
AU  - Petia Radeva
PY  - 2010//
TI  - Error-Correcting Output Codes Library
T2  - JMLR
JO  - Journal of Machine Learning Research
SP  - 661
EP  - 664
VL  - 11
N2  - (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.
SN  - 1532-4435
UR  - http://dl.acm.org/citation.cfm?id=1756026
N1  - MILAB;HUPBA
ID  - Sergio Escalera2010
ER  -