TY  - CHAP
AU  - Sergio Escalera
AU  - David M.J. Tax
AU  - Oriol Pujol
AU  - Petia Radeva
AU  - Robert P.W. Duin
ED  - H. Kawasnicka
ED  - L.Jain
PY  - 2011//
TI  - Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes
BT  - Innovations in Intelligent Image Analysis
SP  - 7
EP  - 29
VL  - 339
PB  - Springer Berlin Heidelberg
CY  - Berlin
N2  - A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
SN  - 1860-949X
SN  - 978-3-642-17933-4
UR  - http://dx.doi.org/10.1007/978-3-642-17934-1_2
N1  - MILAB;HuPBA
ID  - Sergio Escalera2011
ER  -