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Authors: Benoît Gandar 1 ; Gaëlle Loosli 2 and Guillaume Deffuant 3

Affiliations: 1 Clermont Université, Université Blaise Pascal and Cemagref de Clermont-Ferrand, France ; 2 Clermont Université, Université Blaise Pascal and CNRS, France ; 3 Cemagref de Clermont-Ferrand, France

ISBN: 978-989-8425-40-9

Keyword(s): Machine Learning, Classification, Space Filling Design, Dispersion.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Recent theoretical work proposes criteria of dispersion to generate learning points. The aim of this paper is to convince the reader, with experimental proofs, that dispersion is a good criterion in practice for generating learning points for classification problems. Problem of generating learning points consists then in generating points with the lowest dispersion. As a consequence, we present low dispersion algorithms existing in the literature, analyze them and propose a new algorithm.

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Paper citation in several formats:
Gandar, B.; Loosli, G. and Deffuant, G. (2011). DISPERSION EFFECT ON GENERALISATION ERROR IN CLASSIFICATION - Experimental Proof and Practical Algorithm.In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 703-706. DOI: 10.5220/0003293007030706

@conference{icaart11,
author={Benoît Gandar. and Gaëlle Loosli. and Guillaume Deffuant.},
title={DISPERSION EFFECT ON GENERALISATION ERROR IN CLASSIFICATION - Experimental Proof and Practical Algorithm},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={703-706},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003293007030706},
isbn={978-989-8425-40-9},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - DISPERSION EFFECT ON GENERALISATION ERROR IN CLASSIFICATION - Experimental Proof and Practical Algorithm
SN - 978-989-8425-40-9
AU - Gandar, B.
AU - Loosli, G.
AU - Deffuant, G.
PY - 2011
SP - 703
EP - 706
DO - 10.5220/0003293007030706

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