Nonparametric classifier design using greedy tree-structured vector quantization technique

Wen Jyi Hwang*, Bo Yuan Ye, Lin Ying Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In this paper, we propose a novel tree-structured vector quantization (TSVQ) design algorithm for the applications of nonparametric pattern recognition. The TSVQ design algorithm is used to reduce the large size of the design sets required by a nonparametric classifier. For an N-class problem, the TSVQ consists of N branches with one for each class. Using the design sets as training data, the algorithm splits the leaf nodes in a greedy manner to minimize the classification error rate for tree-growing. Simulation results show that the classifiers designed using this new algorithm require less classification time than that required by other design set reduction algorithms. In addition, in many cases, the new classifiers enjoy almost the same low error rate as that of traditional k-NN nonparametric classifiers.

Original languageEnglish
Pages (from-to)409-414
Number of pages6
JournalPattern Recognition Letters
Issue number5
Publication statusPublished - 1997 May
Externally publishedYes


  • k-nearest-neighbor (kNN) classifier
  • Nonparametric classification
  • Vector quantization

ASJC Scopus subject areas

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


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