Mon premier blog

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

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 (Adaptive Computation and Machine Learning series) - The MIT Press - ecs4.com. Smola, Learning with Kernels—Support Vector Machines, Regularization, Optimization and Beyond , MIT Press Series, 2002. 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. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , MIT Press, Cambridge, 2001. 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. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. 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. Tags:Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. 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. 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. Weiterführende Literatur: Abney (2008). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning Series). In the machine learning imagination. 577, 580, Gaussian Processes for Machine Learning (MIT Press).