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This book presents recent research in decision making under
uncertainty, in particular reinforcement learning and learning with
expert advice. The core elements of decision theory, Markov
decision processes and reinforcement learning have not been
previously collected in a concise volume. Our aim with this book
was to provide a solid theoretical foundation with elementary
proofs of the most important theorems in the field, all collected
in one place, and not typically found in introductory
textbooks. This book is addressed to graduate students that
are interested in statistical decision making under uncertainty and
the foundations of reinforcement learning. Â
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Privacy and Security Issues in Data Mining and Machine Learning - International ECML/PKDD Workshop, PSDML 2010, Barcelona, Spain, September 24, 2010. Revised Selected Papers (Paperback, Edition.)
Christos Dimitrakakis, Aris Gkoulalas-Divanis, Aikaterini Mitrokotsa, Vassilios S Verykios, Yucel Saygin
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R1,535
Discovery Miles 15 350
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the International
ECML/PKDD Workshop on Privacy and Security Issues in Data Mining
and Machine Learning, PSDML 2010, held in Barcelona, Spain, in
September 2010. The 11 revised full papers presented were carefully
reviewed and selected from 21 submissions. The papers range from
data privacy to security applications, focusing on detecting
malicious behavior in computer systems.
This book presents recent research in decision making under
uncertainty, in particular reinforcement learning and learning with
expert advice. The core elements of decision theory, Markov
decision processes and reinforcement learning have not been
previously collected in a concise volume. Our aim with this book
was to provide a solid theoretical foundation with elementary
proofs of the most important theorems in the field, all collected
in one place, and not typically found in introductory textbooks.
This book is addressed to graduate students that are interested in
statistical decision making under uncertainty and the foundations
of reinforcement learning.
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