Rule extraction from trained support vector machines

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3518 LNAI, Issue , 2005, Pages 61-70

  Zhang, Ying ^a   Su, HongYe ^a   Jia, Tao ^a   Chu, Jian ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DECISION MAKING; HEURISTIC METHODS; SOFTWARE PROTOTYPING; VECTORS;

RULE EXTRACTION; SUPPORT VECTOR MACHINE (SVM); SUPPORT VECTORS (SV);

LEARNING SYSTEMS;

EID: 26944483485     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11430919_9     Document Type: Conference Paper

References (13)

1. Vapnik V. The Nature of Statistical Learning Theory. Springer, New York, 1995.
2. Craven M. W. and Shavlik J. W. Using Sampling and Queries to Extract Rules From Trained Neural Networks. In Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ, pages 37-45, 1994.
3. R. Andrews, J. Diederich and A. Tickle. A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks, Knowledge-Based Systems, 8(6), pages 373-389, 1995.
4. Fu L. Rule learning by searching on adapted nets. Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, pages 590-595, 1991.
5. H. Núñez, C. Angulo, A. Catala, Rule-extraction from Support Vector Machines, The European Symposiumon Artificial Neural Networks, Burges, pages 107-112, 2002.
6. C.J.C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2): 1-47, 1998.
7. S. Salzberg, A Nearest Hyperrectangle Learning Method, Machine Learning, Kluwer Academic Publisher, Boston, pages 251-276, 1991.
8. Wettschereck D, Dietterich T. An Experimental Comparison of The Nearest-Neighbor and Nearest-hyperrectangle Algorithms. Machine Learning, 19(1):5-27, 1995.
9. A.Ben-Hui D, Horn H.T.Sidgelmann et.al. Support vector clustering. Joumel of machine learning research, pages 125-137, 2001.
10. D.M.J. Tax and R.P.W. Duin. Data Domain Description by Support Vectors. In Proceedings ESANN, pages 251-256, 1999.
11. Bernhard Schölkopf and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002.
12. C. J. Merz and P. M. Murphy, UCI Repository for Machine Learning Data-Bases. Irvine, CA: University of California, Department of Information and Computer Science, http://www.ics.uci.edu/-mlearn/MLRepository.htrnl, 1998.
13. D. Michie, D. J. Spiegelhalter, and C. C. Taylor. Machine Learning, Neural and Statistical Classification. Prentice Hall, Englewood Cliffs, N.J., 1994. Data available at http://www.ncc.up.pt/liacc/ML/statlog/datasets.html.