Statistical Reinforcement Learning_ Modern Machine Learning Approaches [Sugiyama 2015-03-26].pdf

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STATISTICAL
REINFORCEMENT
LEARNING
Modern Machine
Learning Approaches
Chapman & Hall/CRC
Machine Learning & Pattern Recognition Series
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Ralf Herbrich
Amazon Development Center
Berlin, Germany
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PUBLISHED TITLES
BAYESIAN PROGRAMMING
Pierre Bessière, Emmanuel Mazer, Juan-Manuel Ahuactzin, and Kamel Mekhnacha
UTILITY-BASED LEARNING FROM DATA
Craig Friedman and Sven Sandow
HANDBOOK OF NATURAL LANGUAGE PROCESSING, SECOND EDITION
Nitin Indurkhya and Fred J. Damerau
COST-SENSITIVE MACHINE LEARNING
Balaji Krishnapuram, Shipeng Yu, and Bharat Rao
COMPUTATIONAL TRUST MODELS AND MACHINE LEARNING
Xin Liu, Anwitaman Datta, and Ee-Peng Lim
MULTILINEAR SUBSPACE LEARNING: DIMENSIONALITY REDUCTION OF
MULTIDIMENSIONAL DATA
Haiping Lu, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos
MACHINE LEARNING: An Algorithmic Perspective, Second Edition
Stephen Marsland
SPARSE MODELING: THEORY, ALGORITHMS, AND APPLICATIONS
Irina Rish and Genady Ya. Grabarnik
A FIRST COURSE IN MACHINE LEARNING
Simon Rogers and Mark Girolami
STATISTICAL REINFORCEMENT LEARNING: MODERN MACHINE LEARNING APPROACHES
Masashi Sugiyama
MULTI-LABEL DIMENSIONALITY REDUCTION
Liang Sun, Shuiwang Ji, and Jieping Ye
REGULARIZATION, OPTIMIZATION, KERNELS, AND SUPPORT VECTOR MACHINES
Johan A. K. Suykens, Marco Signoretto, and Andreas Argyriou
ENSEMBLE METHODS: FOUNDATIONS AND ALGORITHMS
Zhi-Hua Zhou
Thore Graepel
Microsoft Research Ltd.
Cambridge, UK
Chapman & Hall/CRC
Machine Learning & Pattern Recognition Series
STATISTICAL
REINFORCEMENT
LEARNING
Modern Machine
Learning Approaches
Masashi Sugiyama
University of Tokyo
Tokyo, Japan
CRC Press
Taylor & Francis Group
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Boca Raton, FL 33487-2742
© 2015 by Taylor & Francis Group, LLC
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Version Date: 20150128
International Standard Book Number-13: 978-1-4398-5690-1 (eBook - PDF)
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