An Improved Way to Make Large-Scale SVR Learning Practical
<p/> <p>We first put forward a new algorithm of reduced support vector regression (RSVR) and adopt a new approach to make a similar mathematical form as that of support vector classification. Then we describe a fast training algorithm for simplified support vector regression, sequential...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2004-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/S1110865704312096 |
| Summary: | <p/> <p>We first put forward a new algorithm of reduced support vector regression (RSVR) and adopt a new approach to make a similar mathematical form as that of support vector classification. Then we describe a fast training algorithm for simplified support vector regression, sequential minimal optimization (SMO) which was used to train SVM before. Experiments prove that this new method converges considerably faster than other methods that require the presence of a substantial amount of the data in memory.</p> |
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| ISSN: | 1687-6172 1687-6180 |