Design of decision tree via kernelized hierarchical clustering for multiclass support vector machines

Cybernetics and Systems

Volumn 38, Issue 2, 2007, Pages 187-202

  Lu, Zhao ^a   Lin, Feng ^b   Ying, Hao ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b Wayne State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ERRORS; DECISION TREE; RISK MINIMIZATION; SUPPORT VECTOR MACHINE (SVM);

DECISION TABLES; ERROR ANALYSIS; LEARNING SYSTEMS; OPTIMIZATION; PATTERN RECOGNITION; PROBLEM SOLVING; TREES (MATHEMATICS);

HIERARCHICAL SYSTEMS;

EID: 33847376395     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/01969720601139058     Document Type: Article

References (20)

1. Abe, S. 2005. Support vector machines for pattern classification. London: Springer Verlag.
2. Baudat, G. and F. Anouar. 2000. Generalized discriminant analysis using a kernel approach. Neural Computation, 12:2385-2404.
3. Cheong, S., S. H. Oh, and S. Y. Lee. 2004. Support vector machines with binary tree architecture for multi-class classification. Neural Information Processing-Letters and Reviews, 2(3):47-51.
4. Cristianini, N. and J. Shawe-Taylor. 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.
5. Eiter, T. and H. Mannila. 1997. Distance measures for point sets and their computation. Acta Informatica, 34:109-133.
6. Franc, V. and V. Hlavac. 2004. Statistical pattern recognition toolbox for MATLAB. Prague, Czech: Center for Machine Perception, Czech Technical University.
7. Girolami, M. 2002. Mercer kernel-based clustering in feature space. IEEE Tranactions. on Neural Networks, 13(3):780-784.
8. Hsu, C. W. and C. J. Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2):415-425.
9. King, R., C. Feng, and A. Shutherland. 1995. Statlog: Comparison of classification algorithms on large real-world problems. Applied Artificial Intelligence, 9:289-333.
10. Müller, K. R., S. Mika, G. Ratsch, K. Tsuda, and B. Schölkopf. 2001. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-202.
11. Pekalsak, E., P. Paclik, and R. P. W. Duin. 2001. A generalized kernel approach to dissimilarity-based classification. Journal of Machine Learning Research, 2:175-211.
12. Roth, V. and V. Steinhage. 1999. Nonlinear discriminant analysis using kernel functions. In Advances in neural information processing systems 12, edited by S. A. Solla, T. K. Leen, and K.-R. Müller. Cambridge, MA: MIT Press, pp. 568-574.
13. Schölkopf, B. 2001. The kernel trick for distances In Advances in neural information processing systems 13, edited by T. K. Leen, T. G. Diettrich, and V. Tresp. Cambridge, MA: MIT Press, pp. 301-307.
14. Schölkopf, B. and A. J. Smola. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
15. Schölkopf, B., A. Smola, and K. R. Müller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5): 1299-1319.
16. Theodoridis, S. and K. Koutroumbas. 1998. Pattern recognition. San Diego, CA: Academic Press.
17. Vapnik, V. N. 1998. Statistical learning theory. New York: John Wiley & Sons.
18. Webb, A. R. 2002. Statistical pattern recognition. England: John Wiley & Sons.
19. Weston, J. and C. Watkins. 1999. Support vector machines for multi-class pattern recognition. In Proceedings of the 7th European Symposium on Artificial Neural Networks, Bruges, Belgium.
20. Zhang, D. Q. and S. C. Chen. 2004. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 32:37-50.