TY - JOUR
T1 - Error-correcting ouput codes library
AU - Escalera, Sergio
AU - Pujol, Oriol
AU - Radeva, Petia
PY - 2010/2
Y1 - 2010/2
N2 - 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, β-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.
AB - 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, β-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.
KW - Coding
KW - Decoding
KW - Error-correcting output codes
KW - Matlab
KW - Multi-class classification
KW - Octave
KW - Open source
UR - http://www.scopus.com/inward/record.url?scp=77949498817&partnerID=8YFLogxK
M3 - Journal article
AN - SCOPUS:77949498817
SN - 1532-4435
VL - 11
SP - 661
EP - 664
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
ER -