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.
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-velopment of practical Bayesian methods for challenging learning problems.
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