Citeseer:http://citeseer.istpsu.edu/ Nips:http://books.nips.cc/ I enjoyed reading papers with innovating ideas. In understanding and in- cluding the papers into my book, they were especially helpful if they were well written in that in the abstract and the introduction their proposed methods were explained not by their characteristics but by their approaches and ideas and in the main text the ideas behind the algorithms were explained before their detailed explanation Kobe. October 2004. October 2009 Shigeo Abe References 1. V.N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995 2. V.N. Vapnik. Statistical Learning Theory. John Wiley Sons, New York, 1998. 3. R. Herbrich. Learning Kenel Classifiers: Theory and Algorithms. MIT Press, Cam- ge,MA,2002. 4. B. Scholkopf and A. J Smola. Learning with Kernels: Support Vector Machines, Reg- ularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002 5. S. Young and T Downs. CARVE-A constructive algorithm for real-valued examples. IEEE Transactions on Neural Networks, 9 (6): 1180-1190, 1998 6. S. Abe. Pattern Classification: Neuro-Fuzzy Methods and Their Comparison. Springe Verlag, London, UK, 2001xii Acknowledgments CiteSeer: http://citeseer.ist.psu.edu/ NIPS: http://books.nips.cc/ I enjoyed reading papers with innovating ideas. In understanding and in￾cluding the papers into my book, they were especially helpful if they were well written in that in the abstract and the introduction their proposed methods were explained not by their characteristics but by their approaches and ideas and in the main text the ideas behind the algorithms were explained before their detailed explanation. Kobe, October 2004, October 2009 Shigeo Abe References 1. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995. 2. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, New York, 1998. 3. R. Herbrich. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, Cam￾bridge, MA, 2002. 4. B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Reg￾ularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002. 5. S. Young and T. Downs. CARVE—A constructive algorithm for real-valued examples. IEEE Transactions on Neural Networks, 9(6):1180–1190, 1998. 6. S. Abe. Pattern Classification: Neuro-Fuzzy Methods and Their Comparison. Springer￾Verlag, London, UK, 2001