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

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. Optimization: Convex Optimization Stephen Boyd, Lieven Vandenberghe Numerical Optimization Jorge Nocedal, Stephen Wright Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Shannon CE: A mathematical theory of communication. Schölkopf B, Smola AJ: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Weiterführende Literatur: Abney (2008). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond · MIT Press, 2001. 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. We use the support vector regression (SVR) method to predict the use of an embryo. Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. 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 series) - The MIT Press - ecs4.com. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Support Vector Machines, Regularization, Optimization, and Beyond .