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Maruyama, Ken-Ichi; Maruyama, Minoru; Miyao, Hidetoshi; Nakano, Yasuaki

A method to make multiple hypotheses with high cumulative recognition rate using SVMs. (English) Zbl 1059.68092

Pattern Recognition 37, No. 2, 241-251 (2004).
Summary: 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.

Cited in 2 Documents

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68T10 Pattern recognition, speech recognition
68W05 Nonnumerical algorithms

Keywords:

Support vector machines; DAGSVM; JEITA-HP; Max-Win algorithm; Rank information; Hybrid method

Software:

SVMlight
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Full Text: DOI

References:

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