Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond by Alexander J. Smola, Bernhard Schlkopf

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Alexander J. Smola, Bernhard Schlkopf ebook
ISBN: 0262194759, 9780262194754
Page: 644
Format: pdf
Publisher: The MIT Press

Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. Weiterführende Literatur: Abney (2008). Support Vector Machines, Regularization, Optimization, and Beyond . "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)" "Bernhard Schlkopf, Alexander J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). 577, 580, Gaussian Processes for Machine Learning (MIT Press). In the machine learning imagination. John Shawe-Taylor, Nello Cristianini. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Bernhard Schlkopf, Alexander J. Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond. 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. 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.