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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). Novel indices characterizing graphical models of residues were B. Weiterführende Literatur: Abney (2008). Shannon CE: A mathematical theory of communication. Optimization: Convex Optimization Stephen Boyd, Lieven Vandenberghe Numerical Optimization Jorge Nocedal, Stephen Wright Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. Support Vector Machines, Regularization, Optimization, and Beyond . Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Core Method: Kernel Methods for Pattern Analysis John Shawe-Taylor, Nello Cristianini Learning with Kernels : Support Vector Machines, Regularization, Optimizatio n, and Beyond Bernhard Schlkopf, Alexander J. 577, 580, Gaussian Processes for Machine Learning (MIT Press). 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)" "Bernhard Schlkopf, Alexander J. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 1st edition, 2001.