Nonparametric classifier design using greedy tree-structured vector quantization technique

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)409-414
頁數6
期刊Pattern Recognition Letters
18
發行號5
DOIs
出版狀態已發佈 - 1997 五月
對外發佈

ASJC Scopus subject areas

  • 軟體
  • 訊號處理
  • 電腦視覺和模式識別
  • 人工智慧

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