IMMC: Incremental maximum margin criterion

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 725-730

  Yan, Jun ^a   Zhang, Benyu ^c   Yan, Shuicheng ^a   Yang, Qiang ^b   Li, Hua ^a   Chen, Zheng ^c   Xi, Wensi ^d   Fan, Weiguo ^d   Ma, Wei Ying ^c   Cheng, Qiansheng ^a  

^a PEKING UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^c MICROSOFT RESEARCH ASIA   (China)

^d VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Linear Discriminant Analysis (LDA); Maximum Margin Criterion (MMC); Principal Component Analysis (PCA)

Indexed keywords

ADAPTIVE SYSTEMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; CONSTRAINT THEORY; DATA MINING; DATA REDUCTION; INFORMATION RETRIEVAL SYSTEMS; LEARNING SYSTEMS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING;

FISHER DISCRIMINANT ANALYSIS (FDA); INCREMENTAL MAXIMUM MARGIN CRITERION (IMMC); LINEAR DISCRIMINANT ANALYSIS (LDA); MAXIMUM MARGIN CRITERION (MMC);

LEARNING ALGORITHMS;

EID: 19944428028     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. C.L., B. and C.J., M. UCI Repository of machine learning databases Irvine. CA: University of California, Department of Information and Computer Science.
2. Kushner, H.J. and Clark, D.S. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York, 1978.
3. Lewis, D., Yang, Y., Rose, T. and Li, F. RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research.
4. Li, H., Jiang, T. and Zhang, K., Efficient and Robust Feature Extraction by Maximum Margin Criterion. In Proceedings of the Advances in Neural Information Processing Systems 16, (Vancouver, Canada, 2004), MIT Press.
5. Liu, R.-L. and Lu, Y.-L., Incremental Context Mining for Adaptive Document Classification. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (Edmonton, Alberta, Canada, 2002), 599-604.
6. Weng, J., Zhang, Y. and Hwang, W.-S. Candid Covariancefree Incremental Principal Component Analysis. IEEE Trans .Pattern Analysis and Machine Intelligence, 25 (8). 1034-1040.
7. Yu, L. and Liu, H., Efficiently Handling Feature Redundancy in High-Dimensional Data. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (Washington DC., 2003), 685-690.
8. Zhang, Y. and Weng, J. Convergence analysis of complementary candid incremental principal component Analysis, Michigan State University, 2001.