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






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


Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Publisher The MIT Press Author(s) Alexander J. Weiterführende Literatur: Abney (2008). John Shawe-Taylor, Nello Cristianini. "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. Bernhard Schlkopf, Alexander J. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series). Conference on Computer Vision and Pattern Recognition (CVPR), 2001 ↑ Scholkopf and A. Smola, Learning with Kernels—Support Vector Machines, Regularization, Optimization and Beyond , MIT Press Series, 2002. Support Vector Machines, Regularization, Optimization, and Beyond . 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. 577, 580, Gaussian Processes for Machine Learning (MIT Press). Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond.

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