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Authors: Ning Chen 1 and Nuno C. Marques 2

Affiliations: 1 GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto, Portugal ; 2 CENTRIA/Departamento de Informatica, FCT-UNL, Portugal

ISBN: 978-989-8111-66-1

Keyword(s): Learning vector quantization, Self-organizing map, Categorical, Batch SOM.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; 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: Learning vector quantization (LVQ) is a supervised learning algorithm for data classification. Since LVQ is based on prototype vectors, it is a neural network approach particularly applicable in non-linear separation problems. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for mixed numerical and categorical data. Experiments on various data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to deal with categorical data.

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Paper citation in several formats:
Chen N.; C. Marques N. and (2009). A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 77-84. DOI: 10.5220/0001661700770084

@conference{icaart09,
author={Ning Chen and Nuno {C. Marques}},
title={A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001661700770084},
isbn={978-989-8111-66-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA
SN - 978-989-8111-66-1
AU - Chen, N.
AU - C. Marques, N.
PY - 2009
SP - 77
EP - 84
DO - 10.5220/0001661700770084

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