@inproceedings{295fbc6139ab4cc2b019b8021e28d5ed,
title = "Loss-weighted decoding for error-correcting output coding",
abstract = "The multi-class classification is a challenging problem for several applications in Computer Vision. Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding strategies. The novel decoding strategy is applied to the state-of-the-art coding designs, extensively tested on the UCI Machine Learning repository database and in two real vision applications: tissue characterization in medical images and traffic sign categorization. The results show that the presented methodology considerably increases the performance of the traditional ECOC strategies and the state-of-the-art multi-classifiers.",
keywords = "Classification, Ensemble methods and boosting, Learning, Machine vision applications",
author = "Sergio Escalera and Oriol Pujol and Petia Radeva",
year = "2008",
language = "English",
isbn = "9789898111210",
series = "VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings",
pages = "117--122",
booktitle = "VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings",
note = "3rd International Conference on Computer Vision Theory and Applications, VISAPP 2008 ; Conference date: 22-01-2008 Through 25-01-2008",
}