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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tinghua Wang |
| Copyright Year | 2010 |
| Abstract | This paper presents a new feature weighting method to improve the performance of support vector machine (SVM). The basic idea of this method is to translate the feature weight learning into the problem of choosing a kernel suitable for SVM classification. In more detail, this method tunes the width parameters of Gaussian ARD (Automatic Relevance Determination) kernel via optimizing a kernel evaluation criterion, i.e., kernel polarization. By using gradient ascent technique, each learned parameter indicates the relative importance of the corresponding feature. The proposed method is demonstrated with some UCI machine learning benchmark examples. |
| Starting Page | 518 |
| Ending Page | 521 |
| File Size | 268819 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424472796 |
| e-ISBN | 9781424472802 |
| DOI | 10.1109/ICICTA.2010.108 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-11 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Polarization Automation auto relevance determination (ARD) Gaussian kernel support vector machine (SVM) Mathematics Paper technology feature weighting Support vector machines Learning systems Computer science Support vector machine classification Machine learning Kernel kernel polarization |
| Content Type | Text |
| Resource Type | Article |
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