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

Optimization: Convex Optimization Stephen Boyd, Lieven Vandenberghe Numerical Optimization Jorge Nocedal, Stephen Wright Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Support Vector Machines, Regularization, Optimization, and Beyond . Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. 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. 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. Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Publisher The MIT Press Author(s) Alexander J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). Bernhard Schlkopf, Alexander J. In the machine learning imagination. Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Weiterführende Literatur: Abney (2008). Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. John Shawe-Taylor, Nello Cristianini. 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.