Feature Space Restructuring for SVMs with Application to Text Categorization
by Hiroya Takamura and Yuji Matsumoto

References

Akaike, H. 1974. A New Look at the Statistical Model Identication. IEEE Trans. Autom. Control, vol. AC-19, pp. 716{723.
Amari, S. 1998. Natural Gradient Works Efficiently in Learning. Neural Computation, vol. 10-2, pp. 251{276.
Bell, A. J. and Sejnowski, T. J. 1995. An Information Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 7, 1129{1159.
Bell, A. J. and Sejnowski, T. J. 1997. The 'Independent Components' of Natural Scenes are Edge Filters. Vision Research, 37(23), pp. 3327{3338.
Deerwester, S., Dumais, T., Landauer, T., Furnas, W. and Harshman, A. 1990. Indexing by Latent Semantic Analysis. Journal of the Society for Information Science, 41(6), pp. 391{ 497.
Glenn, F. and Mangasarian, O. 2001. Semi-Supervised Support Vector Machines for Unlabeled Data Classication. Optimization Methods and Software, pp. 1{14.
Herault, J. and Jutten, J. 1986. Space or Time Adaptive Signal Processing by Neural NetworkModels. Neural networks for computing: AIP conference proceedings 151, pp. 206{211.
Isbell, C. and Viola. P. 1998. Restructuring Sparse High Dimensional Data for Eective Retrieval. Advances in Neural Information Processing Systems, volume 11.
Joachims, T. 1998. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of the European Conference on Machine Learning, pp. 137{142.
Joachims, T. 1999a. Transductive Inference for Text Classication using Support Vector Machines. Machine Learning { Proc. 16th Int'l Conf. (ICML '99), pp. 200{209.
Joachims, T. 1999b. Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, pp. 169{ 184.
Kaban, A. and Girolami, M. 2000. Unsupervised Topic Separation and Keyword Identi- cation in Document Collections: A Projection Approach Technical Report.
Kolenda, T, Hansen, L., K. and Sigurdsson, S. 2000. Indepedent Components in Text . Ad- vances in Independent Component Analysis, Springer-Verlag, pp. 235{256.
Mitchell, T. 1997. Machine Learning, McGraw Hill. 
Nigam, K., McCallum, A., Thrun, S. and Mitchell, T. 2000. Text Classication from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3). pp. 103{134.
Rissanen, J. 1987. Stochastic Complexity. Journal of Royal Statistical Society, Series B, 49(3), pp. 223{239.
Salton, G. and McGill, M. J. 1983. Introduction to Modern Information Retrieval. McGraw- Hill Book Company, New York. 
Smola, A., Bartlett, P., Scholkopf, B. and Schuurmans, D. 2000. Advances in Large Margin Classiers. MIT Press 
Vapnik, V. 1995. The Nature of Statistical Learning Theory. Springer.
Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T. and Vapnik, V. 2000. Feature Selection for SVMs. In Advances in Neu- ral Information Processing Systems, volume 13.
Yang, Y. An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval, volume 1, 1-2, pp. 69{90.
