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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Alexander J. Smola, Bernhard Schlkopf
Publisher: The MIT Press

Learning with kernels support vector machines, regularization, optimization, and beyond. Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. Shannon CE: A mathematical theory of communication. Novel indices characterizing graphical models of residues were B. We use the support vector regression (SVR) method to predict the use of an embryo. 577, 580, Gaussian Processes for Machine Learning (MIT Press).