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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 (Adaptive Computation and Machine Learning). 577, 580, Gaussian Processes for Machine Learning (MIT Press). Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. John Shawe-Taylor, Nello Cristianini. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001. In the machine learning imagination. Bernhard Schlkopf, Alexander J. Novel indices characterizing graphical models of residues were B. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. 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. Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. 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.