A Fuzzy based on Parameters determination for Support Vector Machine
碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 100 === In the phase of building SVM model, it is still an unsolved problem of how to decide the optimal parameter values for the cost function and kernel function.Although numerous researches have been proposed to overcome this problem, they were suffered with the pro...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/73781506506309519033 |
| Summary: | 碩士 === 朝陽科技大學 === 資訊工程系碩士班 === 100 === In the phase of building SVM model, it is still an unsolved problem of how to decide the optimal parameter values for the cost function and kernel function.Although numerous researches have been proposed to overcome this problem,
they were suffered with the problem of much time complexity. Ideal parameter values could increase the accuracy of classification. In this thesis a novel algorithm is proposed to generate ideal parameter values. In this algorithm,overall relations between training patterns are summarized into nine fuzzy rules and fuzzy inference engine is used to generate the ideal parameter values.Besides, fuzzy neural network is used to reach the optimal solution.Experimental results show that our proposed algorithm produces ideal C and γ effectively and outperform other methods.
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