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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2011.03243 |
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| _version_ | 1866914932322205696 |
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| author | Kumar, Bagesh Sinha, Ayush Chakrabarti, Sourin Vyas, O. P. |
| author_facet | Kumar, Bagesh Sinha, Ayush Chakrabarti, Sourin Vyas, O. P. |
| contents | One Class Slab Support Vector Machines (OCSSVM) have turned out to be better in terms of accuracy in certain classes of classification problems than the traditional SVMs and One Class SVMs or even other One class classifiers. This paper proposes fast training method for One Class Slab SVMs using an updated Sequential Minimal Optimization (SMO) which divides the multi variable optimization problem to smaller sub problems of size two that can then be solved analytically. The results indicate that this training method scales better to large sets of training data than other Quadratic Programming (QP) solvers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2011_03243 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | A fast learning algorithm for One-Class Slab Support Vector Machines Kumar, Bagesh Sinha, Ayush Chakrabarti, Sourin Vyas, O. P. Machine Learning One Class Slab Support Vector Machines (OCSSVM) have turned out to be better in terms of accuracy in certain classes of classification problems than the traditional SVMs and One Class SVMs or even other One class classifiers. This paper proposes fast training method for One Class Slab SVMs using an updated Sequential Minimal Optimization (SMO) which divides the multi variable optimization problem to smaller sub problems of size two that can then be solved analytically. The results indicate that this training method scales better to large sets of training data than other Quadratic Programming (QP) solvers. |
| title | A fast learning algorithm for One-Class Slab Support Vector Machines |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2011.03243 |