Learning with Kernels: Support Vector Machines,

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
Publisher: The MIT Press
Format: pdf
Page: 644
ISBN: 0262194759, 9780262194754

In the machine learning imagination. Optimization: Convex Optimization Stephen Boyd, Lieven Vandenberghe Numerical Optimization Jorge Nocedal, Stephen Wright Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning). 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. 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. 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. Support Vector Machines, Regularization, Optimization, and Beyond . Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. Weiterführende Literatur: Abney (2008). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.