TY  - JOUR
AU  - Sergio Escalera
AU  - David Masip
AU  - Eloi Puertas
AU  - Petia Radeva
AU  - Oriol Pujol
PY  - 2011//
TI  - Online Error-Correcting Output Codes
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 458
EP  - 467
VL  - 32
IS  - 3
PB  - Elsevier
CY  - North Holland
N2  - IF JCR CCIA 1.303 2009 54/103This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier.
SN  - 0167-8655
UR  - http://dx.doi.org/10.1016/j.patrec.2010.11.005
N1  - MILAB;OR;HuPBA;MV
ID  - Sergio Escalera2011
ER  -