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Authors: Sergio Escalera ; Oriol Pujol and Petia Radeva

Affiliation: Computer Vision Center, Universitat de Barcelona, Spain

Keyword(s): Ensemble Methods and Boosting, Learning, Classification, Machine Vision Applications.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

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.

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Paper citation in several formats:
Escalera, S.; Pujol, O. and Radeva, P. (2008). LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN. In Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: VISAPP; ISBN 978-989-8111-21-0; ISSN 2184-4321, SciTePress, pages 117-122. DOI: 10.5220/0001071601170122

@conference{visapp08,
author={Sergio Escalera. and Oriol Pujol. and Petia Radeva.},
title={LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: VISAPP},
year={2008},
pages={117-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001071601170122},
isbn={978-989-8111-21-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISIGRAPP 2008) - Volume 1: VISAPP
TI - LOSS-WEIGHTED DECODING FOR ERROR-CORRECTING OUTPUT CODIN
SN - 978-989-8111-21-0
IS - 2184-4321
AU - Escalera, S.
AU - Pujol, O.
AU - Radeva, P.
PY - 2008
SP - 117
EP - 122
DO - 10.5220/0001071601170122
PB - SciTePress

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