Transcription of Gaussian Processes for Machine Learning
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Gaussian Processes for Machine Learning
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of Processes for Machine LearningC. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning , the MIT Press, 2006,ISBN 2006 Massachusetts Institute of Computation and Machine LearningThomas Dietterich, EditorChristopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate EditorsBioinformatics: The Machine Learning Approach,Pierre Baldi and S ren BrunakReinforcement Learning : An Introduction,Richard S. Sutton and Andrew G. BartoGraphical Models for Machine Learning and Digital Communication,Brendan J. FreyLearning in Graphical Models,Michael I. JordanCausation, Prediction, and Search, second edition,Peter Spirtes, Clark Glymour, and Richard ScheinesPrinciples of Data Mining,David Hand, Heikki Mannila, and Padhraic SmythBioinformatics: The Machine Learning Approach, second edition,Pierre Baldi and S ren BrunakLearning Kernel Classifiers: Theory and Algorithms,Ralf HerbrichLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond,Bernhard Sch olkopf and Alexander J.
velopment of practical Bayesian methods for challenging learning problems. Gaussian Processes for Machine Learning presents one of the most important Bayesian machine learning approaches based on a particularly effective method for placing a prior distribution over the space of functions. Carl Edward Ras-
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