PT Journal
AU Sergio Escalera
   David Masip
   Eloi Puertas
   Petia Radeva
   Oriol Pujol
TI Online Error-Correcting Output Codes
SO Pattern Recognition Letters
JI PRL
PY 2011
BP 458
EP 467
VL 32
IS 3
DI 10.1016/j.patrec.2010.11.005
AB 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.
PI North Holland
ER