著者

小林 正幸 小西 康夫 石垣 博行

出版者

一般社団法人日本機械学会

雑誌

日本機械学會論文集. C編 (ISSN:03875024)

巻号頁・発行日

vol.71, no.710, pp.2989-2995, 2005-10-25

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Support Vector Machines (SVM) are learning machines with high performance about pattern recognition. But, SVM has some parameters decided preliminarily. As these parameters vary, SVM's recognition performance severely varies. In this paper, we propose an optimizing method of SVM's parameters using Reinforcement Learning. By the proposed method, the Actor-Critic method that is a kind of reinforcement learning was used. Actor-Critic method can use continuous action space. The effectiveness of the proposed method is confirmed by a one-dimension pattern recognition simulation. And, we verify practical effectiveness of the proposed method by experiments about handwritten character recognition. As results, the recognition rate of characters increased to about 90% with parameters of SVM optimized by the proposed method.