CASIA OpenIR  > 09年以前成果
Some marginal learning algorithms for unsupervised problems
Tao, Q; Wu, GW; Wang, FY; Wang, J; Kantor, P; Muresan, G; Roberts, F; Zeng, DD; Wang, FY; Chen, H; Merkle, RC
Source PublicationINTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS
2005
Volume3495Pages:395-401
SubtypeArticle
AbstractIn 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 HeadingsScience & Technology ; Technology
WOS KeywordSUPPORT
Indexed ByISTP ; SCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods
WOS IDWOS:000230114100034
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9114
Collection09年以前成果
Affiliation1.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
Recommended Citation
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.
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