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Authors: Atsushi Sato and Masato Ishii

Affiliation: NEC Corporation 1753, Japan

ISBN: 978-989-8565-41-9

Keyword(s): Lorentzian Mixture, Nearest Neighbor, Loss Minimization, Bayes Decision Theory, Machine Learning.

Related Ontology Subjects/Areas/Topics: Classification ; Pattern Recognition ; Theory and Methods

Abstract: This paper presents a novel distance-based classifier based on the multiplicative inverse of Lorentzian mixture, which can be regarded as a natural extension of the conventional nearest neighbor rule. We show that prototypes and weights can be trained simultaneously by General Loss Minimization, which is a generalized version of supervised learning framework used in Generalized Learning Vector Quantization. Experimental results for UCI machine learning repository reveal that the proposed method achieves almost the same as or higher classification accuracy than Support Vector Machine with a much fewer prototypes than support vectors.

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Paper citation in several formats:
Sato, A. and Ishii, M. (2013). Inverse of Lorentzian Mixture for Simultaneous Training of Prototypes and Weights.In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 151-158. DOI: 10.5220/0004240201510158

@conference{icpram13,
author={Atsushi Sato. and Masato Ishii.},
title={Inverse of Lorentzian Mixture for Simultaneous Training of Prototypes and Weights},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={151-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004240201510158},
isbn={978-989-8565-41-9},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Inverse of Lorentzian Mixture for Simultaneous Training of Prototypes and Weights
SN - 978-989-8565-41-9
AU - Sato, A.
AU - Ishii, M.
PY - 2013
SP - 151
EP - 158
DO - 10.5220/0004240201510158

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