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Algorithmic Learning Theory - 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings (Paperback)
Jyriki Kivinen, Csaba Szepesvari, Esko Ukkonen, Thomas Zeugmann
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R1,612
Discovery Miles 16 120
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 22nd
International Conference on Algorithmic Learning Theory, ALT 2011,
held in Espoo, Finland, in October 2011, co-located with the 14th
International Conference on Discovery Science, DS 2011.
The 28 revised full papers presented together with the abstracts of
5 invited talks were carefully reviewed and selected from numerous
submissions. The papers are divided into topical sections of papers
on inductive inference, regression, bandit problems, online
learning, kernel and margin-based methods, intelligent agents and
other learning models.
Reinforcement learning is a learning paradigm concerned with
learning to control a system so as to maximize a numerical
performance measure that expresses a long-term objective. What
distinguishes reinforcement learning from supervised learning is
that only partial feedback is given to the learner about the
learner's predictions. Further, the predictions may have long term
effects through influencing the future state of the controlled
system. Thus, time plays a special role. The goal in reinforcement
learning is to develop efficient learning algorithms, as well as to
understand the algorithms' merits and limitations. Reinforcement
learning is of great interest because of the large number of
practical applications that it can be used to address, ranging from
problems in artificial intelligence to operations research or
control engineering. In this book, we focus on those algorithms of
reinforcement learning that build on the powerful theory of dynamic
programming. We give a fairly comprehensive catalog of learning
problems, describe the core ideas, note a large number of state of
the art algorithms, followed by the discussion of their theoretical
properties and limitations. Table of Contents: Markov Decision
Processes / Value Prediction Problems / Control / For Further
Exploration
Decision-making in the face of uncertainty is a significant
challenge in machine learning, and the multi-armed bandit model is
a commonly used framework to address it. This comprehensive and
rigorous introduction to the multi-armed bandit problem examines
all the major settings, including stochastic, adversarial, and
Bayesian frameworks. A focus on both mathematical intuition and
carefully worked proofs makes this an excellent reference for
established researchers and a helpful resource for graduate
students in computer science, engineering, statistics, applied
mathematics and economics. Linear bandits receive special attention
as one of the most useful models in applications, while other
chapters are dedicated to combinatorial bandits, ranking,
non-stationary problems, Thompson sampling and pure exploration.
The book ends with a peek into the world beyond bandits with an
introduction to partial monitoring and learning in Markov decision
processes.
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