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

Shannon CE: A mathematical theory of communication. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , MIT Press, Cambridge, 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). Novel indices characterizing graphical models of residues were B. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Bernhard Schlkopf, Alexander J. John Shawe-Taylor, Nello Cristianini. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). In the machine learning imagination. Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. Smola, Learning with Kernels—Support Vector Machines, Regularization, Optimization and Beyond , MIT Press Series, 2002. Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Each is important even without the other: kernels are useful all over and support vector machines would be useful even if we restricted to the trivial identity kernel. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning series) - The MIT Press - ecs4.com. Partly this is because a number of good ideas are overly associated with them: support/non-support training datums, weighting training data, discounting data, regularization, margin and the bounding of generalization error. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond.