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Knowledge based support vector machines
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ibaraki, Toshihide Fukunaga, Takuro |
| Copyright Year | 2005 |
| Abstract | Support vector machines (SVMs), especially nonlinear SVMs, are known to have high performance as classifiers of data. In this paper, we propose to construct a nonlinear SVM from a set of available prior knowledge on the problem domain and to determine their weights by using training data set, which we call the knowledge based SVM (KSVM). A basic tool for KSVM is the reduced SVM (RSVM) proposed by Y. -J. Lee and 0. L. Mangasarian, in which kernel functions represent such knowledge. A KSVM has an advantage that its behavior is highly understandable as we can see how the kernels representing prior knowledge are combined into a classifier. It is confirmed by computational experiments that KSVMs can have high performance. We also discuss the separability condition and theVC dimension of KSVM. |
| Starting Page | 259 |
| Ending Page | 268 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| Volume Number | 13 |
| Alternate Webpage(s) | https://ist.ksc.kwansei.ac.jp/rchm/57_001.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
