A vector quantization method for nearest neighbor classifier design

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

Research output: Contribution to journalArticlepeer-review

11 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 languageEnglish
Pages (from-to)725-731
Number of pages7
JournalPattern Recognition Letters
Volume25
Issue number6
DOIs
StatePublished - 19 Apr 2004
Externally publishedYes

Keywords

  • Classification
  • Nearest neighbor classifier
  • Supervised learning
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A vector quantization method for nearest neighbor classifier design'. Together they form a unique fingerprint.

Cite this