Applications of feature space distance measures in training support vector classifiers
博士 === 國立臺灣大學 === 電機工程學研究所 === 96 === Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. The most popular method to decide the parameters is the grid search method. In the training process, classifiers are trained with differ...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/66064360761867794430 |