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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wan Zhang Irwin King |
| Copyright Year | 2002 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China (Wan Zhang; Irwin King) |
| Abstract | One of the major tasks in the support vector machine (SVM) algorithm is to locate the discriminant boundary in classification task. It is crucial to understand various approaches to this particular task. In this paper, we survey several different methods of finding the boundary from different disciplines. In particular, we examine SVM from the statistical learning theory, the convex hull problem from the computational geometry's point of view, and Gabriel's graph from the computational geometry perspective to describe their theoretical connections and practical implementation implications. Moreover, we implement these methods and demonstrate their respective results on the classification accuracy and run time complexity. Finally, we conclude with some discussions about these three different techniques. |
| Sponsorship | IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc |
| Starting Page | 239 |
| Ending Page | 244 |
| File Size | 400585 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372786 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2002.1005476 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Support vector machine classification Neural networks Kernel Lagrangian functions Computer science Statistical learning Risk management Machine learning Data mining |
| Content Type | Text |
| Resource Type | Article |
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