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, R

rounding supervised, unsupervised, and reinforcement learning problems. The MIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications. One of the most active directions in machine learning has been the de-

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  Processes, Machine, Learning, Reinforcement, Gaussian processes for machine learning, Gaussian, Reinforcement learning

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