Pattern Recognition, Vol. 37, pp.241-251 (2004)
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 bee shown that Directed Acyclic Graph Support Vector Machines (DAGSVM) is very good with respect to recognition rate, learning time and evaluation time. Hoever, 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 theresult of DAGSVM, a limited set of classes are extracted. Then, Max-Win algorithm is applied to the set. We also provide the exprimental results to show that the cumulative recognition rage of our method is as good as the Max-Win algorithm, and that the execution time is as fas as DAGSVM.
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First Written February 24, 2004
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