A vector quantization method for nearest neighbor classifier design

Chen Wen Yen; Chieh Neng Young; Mark L. Nagurka

doi:10.1016/j.patrec.2004.01.012

A vector quantization method for nearest neighbor classifier design

Chen Wen Yen, Chieh Neng Young, Mark L. Nagurka

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method.

Original language	English
Pages (from-to)	725-731
Number of pages	7
Journal	Pattern Recognition Letters
Volume	25
Issue number	6
DOIs	https://doi.org/10.1016/j.patrec.2004.01.012
State	Published - 19 Apr 2004
Externally published	Yes

Keywords

Classification
Nearest neighbor classifier
Supervised learning
Vector quantization

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patrec.2004.01.012

Cite this

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title = "A vector quantization method for nearest neighbor classifier design",

abstract = "This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method.",

keywords = "Classification, Nearest neighbor classifier, Supervised learning, Vector quantization",

author = "Yen, {Chen Wen} and Young, {Chieh Neng} and Nagurka, {Mark L.}",

note = "Funding Information: This research was supported in part by National Science Council of Republic of China under grant number NSC 89-2212-E110-020.",

year = "2004",

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AU - Yen, Chen Wen

AU - Young, Chieh Neng

AU - Nagurka, Mark L.

N1 - Funding Information: This research was supported in part by National Science Council of Republic of China under grant number NSC 89-2212-E110-020.

PY - 2004/4/19

Y1 - 2004/4/19

N2 - This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method.

AB - This paper proposes a nearest neighbor classifier design method based on vector quantization (VQ). By investigating the error distribution pattern of the training set, the VQ technique is applied to generate prototypes incrementally until the desired classification result is reached. Experimental results demonstrate the effectiveness of the method.

KW - Classification

KW - Nearest neighbor classifier

KW - Supervised learning

KW - Vector quantization

UR - http://www.scopus.com/inward/record.url?scp=1842787628&partnerID=8YFLogxK

U2 - 10.1016/j.patrec.2004.01.012

DO - 10.1016/j.patrec.2004.01.012

M3 - Article

AN - SCOPUS:1842787628

SN - 0167-8655

VL - 25

SP - 725

EP - 731

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 6

ER -

A vector quantization method for nearest neighbor classifier design

Abstract

Keywords

ASJC Scopus subject areas

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