Reduce the number of support vectors by using culstering techniques

International Conference on Machine Learning and Cybernetics

Volumn 2, Issue , 2003, Pages 1245-1248

  Tran, Quang Anh ^a   Zhang, Qian Li ^a   Li, Xing ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Clustering Svm; Intrusion detection; K means clustering; Support Vector Machine

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DECISION MAKING;

INTRUSION DETECTION; K-MEANS CLUSTERING; SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 1542376001     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. V.N. Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999
2. T. Joachims. Text categorization with support vector machine: Learning with many relevant features. Proceedings of the European Conference on Machine Learning, 1998.
3. H. Drucker, D. Wu, V.N. Vapnik. IEEE Transactions on Neural Networks, 1999, 10(5): 1048-1054
4. B. Scholkopf, A. Smola, R.C. Williamson, et al. New support vector algorithms. Neural Computation, 2000, 12: 1207-1245