Low-degree Polynomial Mapping of Data for SVM
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 97 === Non-linear mapping functions have long been used in SVM to transform data into a higher dimensional space, allowing the classifier to separate non-linearly distributed data instances. Kernel tricks are used to avoid the problem of a huge number of features of th...
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| Format: | Others |
| Language: | en_US |
| Published: |
2009
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| Online Access: | http://ndltd.ncl.edu.tw/handle/85416613440796675619 |