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Authors: Wladyslaw Homenda 1 ; Agnieszka Jastrzebska 2 and Witold Pedrycz 3

Affiliations: 1 Warsaw University of Technology and University of Bialystok, Poland ; 2 Warsaw University of Technology, Poland ; 3 Polish Academy of Sciences and University of Alberta, Poland

ISBN: 978-989-758-074-1

Keyword(s): Pattern Recognition, Rejection Option, Native and Foreign Elements, Support Vector Machines.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge Representation and Reasoning ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Model-Based Reasoning ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Standard assumption of pattern recognition problem is that processed elements belong to recognized classes. However, in practice, we are often faced with elements presented to recognizers, which do not belong to such classes. For instance, paper-to-computer recognition technologies (e.g. character or music recognition technologies, both printed and handwritten) must cope with garbage elements produced at segmentation level. In this paper we distinguish between elements of desired classes and other ones. We call them native and foreign elements, respectively. The assumption that we have only native elements results in incorrect inclusion of foreign ones into desired classes. Since foreign elements are usually not known at the stage of recognizer construction, standard classification methods fail to eliminate them. In this paper we study construction of recognizers based on support vector machines and aimed on coping with foreign elements. Several tests are performed on real-world data.

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Paper citation in several formats:
Homenda, W.; Jastrzebska, A. and Pedrycz, W. (2015). Rejecting Foreign Elements in Pattern Recognition Problem - Reinforced Training of Rejection Level.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 90-99. DOI: 10.5220/0005207900900099

@conference{icaart15,
author={Wladyslaw Homenda. and Agnieszka Jastrzebska. and Witold Pedrycz.},
title={Rejecting Foreign Elements in Pattern Recognition Problem - Reinforced Training of Rejection Level},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={90-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005207900900099},
isbn={978-989-758-074-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Rejecting Foreign Elements in Pattern Recognition Problem - Reinforced Training of Rejection Level
SN - 978-989-758-074-1
AU - Homenda, W.
AU - Jastrzebska, A.
AU - Pedrycz, W.
PY - 2015
SP - 90
EP - 99
DO - 10.5220/0005207900900099

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