Some marginal learning algorithms for unsupervised problems
文献类型：期刊论文
| 作者 | Tao, Q; Wu, GW ; Wang, FY; Wang, J; Kantor, P; Muresan, G; Roberts, F; Zeng, DD; Wang, FY; Chen, H
|
| 刊名 | INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS
 |
| 出版日期 | 2005 |
| 卷号 | 3495页码:395-401 |
| 英文摘要 | In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold eta is formally described and the intuitive margin is introduced. Then, one-class and clustering problems are formulated as two specific eta-unsupervised problems. By defining a specific hypothesis space in eta-one-class problems, the crucial minimal sphere algorithm for regular one-class problems is proved to be a maximum margin algorithm. Furthermore, some new one-class and clustering marginal algorithms can be achieved in terms of different hypothesis spaces. Since the nature in SVMs is employed successfully, the proposed algorithms have robustness, flexibility and high performance. Since the parameters in SVMs are interpretable, our unsupervised learning framework is clear and natural. To verify the reasonability of our formulation, some synthetic and real experiments are conducted. They demonstrate that the proposed framework is not only of theoretical interest, but they also has a legitimate place in the family of practical unsupervised learning techniques. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | SUPPORT |
| 收录类别 | ISTP ; SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000230114100034 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9114]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligenct Informat Proc, Bioinformat Res Grp, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tao, Q,Wu, GW,Wang, FY,et al. Some marginal learning algorithms for unsupervised problems[J]. INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS,2005,3495:395-401. |
| APA | Tao, Q.,Wu, GW.,Wang, FY.,Wang, J.,Kantor, P.,...&Merkle, RC.(2005).Some marginal learning algorithms for unsupervised problems.INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS,3495,395-401. |
| MLA | Tao, Q,et al."Some marginal learning algorithms for unsupervised problems".INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS 3495(2005):395-401. |
入库方式： OAI收割
来源：自动化研究所
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