Two-level hierarchical hybrid SVM-RVM classification model

Proceedings - 5th International Conference on Machine Learning and Applications, ICMLA 2006

Volumn , Issue , 2006, Pages 89-94

  Silva, Catarina ^a,b   Ribeiro, Bernardete ^b  

^a POLYTECHNIC INSTITUTE OF LEIRIA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; HIERARCHICAL SYSTEMS;

KERNEL SELECTION; RELEVANCE VECTOR MACHINES (RVM);

SUPPORT VECTOR MACHINES;

EID: 40349091811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2006.52     Document Type: Conference Paper

References (10)

1. http://kdd.ics.uci.edu/databases/reuters21578/reuters21578. html.
2. S. Eyheramendy, A. Genkin, W. Ju, D. Lewis, and D. Madigan. Sparse bayesian classifi ers for text classifi cation. Journal of Intelligence Community Research and Development, 2003.
3. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML), pages 137-142. Springer, 1998.
4. B. Schölkopf, C. Burges, and A. Smola. Advances in Kernel Methods: Support Vector Machines. MIT Press, Erewhon, NC, 1998.
5. F. Sebastiani. Classifi cation of text, automatic. The Encyclopedia of Language and Linguistics, Keith Brown (ed.), Volume 14, 2nd Edition, Elsevier Science Publishers, Amsterdam, NL, 2006.
6. C. Silva and B.Ribeiro. Scaling text classifi cation with relevance vector machines. In Proceedings of the IEEE Internatinal Conference on Systems, Man and Cybernetics, 2006.
7. M. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research I, pages 211-214, 2001.
8. V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1999.
9. L. Wei, Y. Yang, R. Nishikawa, M. Wernick, and A. Edwards. Relevance vector machine for automatic detection of clustered microcalcifi cations. IEEE Transactions on Medical Imaging, 24(10):1278-1285, 2005.
10. O. Williams, A. Blake, and R. Cipolla. Sparse bayesian learning for effi cient visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8): 1292-1304, 2005.