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 |
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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 |
| _version_ | 1818322861425688576 |
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| author | Yong Quan Jie Yang Lixiu Yao Chenzhou Ye |
| author_facet | Yong Quan Jie Yang Lixiu Yao Chenzhou Ye |
| author_sort | Yong Quan |
| collection | DOAJ |
| description | <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> |
| first_indexed | 2024-12-13T11:03:31Z |
| format | Article |
| id | doaj.art-3d73693a97e848408b1bf4ca907623e1 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-13T11:03:31Z |
| publishDate | 2004-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-3d73693a97e848408b1bf4ca907623e12022-12-21T23:49:09ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802004-01-0120048723740An Improved Way to Make Large-Scale SVR Learning PracticalYong QuanJie YangLixiu YaoChenzhou Ye<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>http://dx.doi.org/10.1155/S1110865704312096RSVRSVMsequential minimal optimization |
| spellingShingle | Yong Quan Jie Yang Lixiu Yao Chenzhou Ye An Improved Way to Make Large-Scale SVR Learning Practical EURASIP Journal on Advances in Signal Processing RSVR SVM sequential minimal optimization |
| title | An Improved Way to Make Large-Scale SVR Learning Practical |
| title_full | An Improved Way to Make Large-Scale SVR Learning Practical |
| title_fullStr | An Improved Way to Make Large-Scale SVR Learning Practical |
| title_full_unstemmed | An Improved Way to Make Large-Scale SVR Learning Practical |
| title_short | An Improved Way to Make Large-Scale SVR Learning Practical |
| title_sort | improved way to make large scale svr learning practical |
| topic | RSVR SVM sequential minimal optimization |
| url | http://dx.doi.org/10.1155/S1110865704312096 |
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