Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond epub
Par carter kristin le mercredi, juillet 13 2016, 07:31 - Lien permanent
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
Page: 644
Format: pdf
ISBN: 0262194759, 9780262194754
Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. Shannon CE: A mathematical theory of communication. Will Read Data Mining: Practical Machine Learning Tools and Techniques 难度低使用 Kernel. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). In the machine learning imagination. 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. Weiterführende Literatur: Abney (2008). Applying Knowledge Management Techniques for Building Corporate Memories http://rapidshare.com/files/117882794/book56.rar. 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. 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.