A novel support vector machine with its features weighted by mutual information

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2008, Pages 315-320

  Xing, Hong Jie ^a   Ha, Ming Hu ^a   Tian, Da Zeng ^a   Hu, Bao Gang ^b  

^a HEBEI UNIVERSITY   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; IMAGE RETRIEVAL; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; NUMERICAL ANALYSIS; VECTORS;

BENCHMARK CLASSIFICATIONS; COMPUTATIONAL COSTS; FEATURE WEIGHTS; GENERALIZATION PERFORMANCES; MUTUAL INFORMATIONS; NUMERICAL STUDIES; SUPPORT VECTORS; WEIGHTED FEATURES;

SUPPORT VECTOR MACHINES;

EID: 56349126904     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2008.4633810     Document Type: Conference Paper

References (26)

1. C. Burges, "A tutorial on support vector machines for pattern recognition", Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998.
2. V. N. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
3. V. N. Vapnik, Statistical Learning Theory, New York: Wiley, 1998.
4. C. F. Tsai, "Training support vector machines based on stacked generalization for image classification", Neurocomputing, vol. 64, pp. 497-503, 2005.
5. Y. Q. Zhang and D. G. Shen, "Design efficient support vector machine for fast classification", Pattern Recognition, vol. 38, no. 1, pp. 157-161, 2005.
6. V. N. Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation and signal processing", Advances in Neural Information Processing Systems, vol. 9, Cambridge, Mass: MIT Press, 1997.
7. J. J. Tsong, "Hybrid approach of selecting hyper-parameters of support vector machine for regression", IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, no. 3, pp. 699-709, 2006.
8. A. Çelikyilmaz and I. B. Türkşen, "Fuzzy functions with support vector machines", Information Sciences, vol. 177, pp. 5163-5177, 2007.
9. B. Schölkopf and A. J. Smola, Learning with Kernels, MIT Press, MA, USA, 2002.
10. X. Wang and Q. He, "Enhancing generalization capability of SVM classifiers with feature weight adjustment", Lecture Notes in Computer Science, vol. 3213, pp. 1037-1043, 2004.
11. J. Yao, S. Zhao, and L. Fan, "An enhanced support vector machine model for intrusion detection", Lecture Note in Computer Science, vol. 4062, pp. 538-542, 2006.
12. H.-G. Chew, R. E. Bogner, and C.-C. Lim, "Dual v-support vector machine with error rate and training sizebiasing", in proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'01), vol. 2, pp. 1269-1272, 2001.
13. C.-F. Lin and S.-D. Wang, "Fuzzy support vector machine", IEEE Trans. on Neural Networks, vol. 13, no. 2, pp. 464-471, 2002.
14. M. Wang, J. Yang, G.-P. Liu, Z.-J. Xu, and K.-C. Chou, "Weighted-support vector machines for predicting membrane protein tyes based on pseudo-amino acid composition", Protein Engineering, Design & Selection, vol. 17, no. 6, pp. 509-516, 2004.
15. J. Principe, D. Xu, and J. Fisher, "Information theoretic learning", in: Unsupervised Adaptive Filtering, Edited by S. Haykin, New York: John Wiley & Sons, 2000.
16. K. Torkkola, "Feature extraction by non-parametric mutual information maximization", Journal of Machine Learning Research, vol. 3, pp. 1415-1438, 2003.
17. N. Kwak and C.-H. Choi, "Input feature selection by mutual information based on Parzen window", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1667-1671, Dec. 2002.
18. S. Cang and D. Partridge, "Feature ranking and best feature subset using mutual information", Neural Computing & Applications, vol. 13, no. 13, pp. 175-184, Sep. 2004.
19. C. E. Shannon and W. Weaver, The Mathematical Theory of Communication, Urbana, IL: University of ILLinois Press, 1949.
20. B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chspman & Hall, London, 1986.
21. S. Knerr, L. Personnaz, and G. Dreyfus, "Single layer learning revisited: a stepwise procedure for building and training a neural network," in: Neurocomputing: Algorithms Architectures and Applications, Edited by F. Fogelman Soulié and J. Hérault, New York, NY: Springer, 1990.
22. B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
23. C. Blake and C Merz, UCI repository of machine learning datasets, 1998. Available from [http://www.ics.uci.edu/~mlearn/MLRepository.html]
24. X. Wang, Y. Wang, and L. Wang, "Improving fuzzy c-means clustering based on feature-weight learning", Pattern Recognition Letters, vol. 25, no. 10, pp. 1123-1132, 2004.
25. Y. Sun and J. Li, "Iterative RELIEF for feature weighting", in: Proc. 23rd International Conference on Machine Learning (ICML'06), pp. 913-920, 2006.
26. Y. Sun, "Iterative RELIEF for feature weighting: algorithm, theories, and application", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1035-1051, Jun. 2007.