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A method to make multiple hypotheses with high cumulative recognition rate using SVMs

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
37
Issue
2
Identifiers
DOI: 10.1016/s0031-3203(03)00236-x
Keywords
  • Support Vector Machines
  • Dagsvm
  • Jeita-Hp
  • Max-Win Algorithm
  • Rank Information
  • Hybrid Method
Disciplines
  • Computer Science

Abstract

Abstract This paper describes a method to make multiple hypotheses with high cumulative recognition rate using SVMs. To make just a single hypothesis by using SVMs, it has been shown that Directed Acyclic Graph Support Vector Machines (DAGSVM) is very good with respect to recognition rate, learning time and evaluation time. However, DAGSVM is not directly applicable to make multiple hypotheses. In this paper, we propose a hybrid method of DAGSVM and Max-Win algorithm. Based on the result of DAGSVM, a limited set of classes are extracted. Then, Max-Win algorithm is applied to the set. We also provide the experimental results to show that the cumulative recognition rate of our method is as good as the Max-Win algorithm, and that the execution time is almost as fast as DAGSVM.

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