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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 series) - The MIT Press - ecs4.com. In the machine learning imagination. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). Support Vector Machines, Regularization, Optimization, and Beyond . Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. Bernhard Schlkopf, Alexander J. 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. 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) (Adaptive Computation and Machine Learning Series). Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). 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. Shannon CE: A mathematical theory of communication. 577, 580, Gaussian Processes for Machine Learning (MIT Press). 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. Weiterführende Literatur: Abney (2008). John Shawe-Taylor, Nello Cristianini.