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This textbook presents a thorough foundation to the theory of
computation. Combining intuitive descriptions and illustrations
with rigorous arguments and detailed proofs for key topics, the
logically structured discussion guides the reader through the core
concepts of automata and languages, computability, and complexity
of computation. Topics and features: presents a detailed
introduction to the theory of computation, complete with concise
explanations of the mathematical prerequisites; provides
end-of-chapter problems with solutions, in addition to
chapter-opening summaries and numerous examples and definitions
throughout the text; draws upon the author's extensive teaching
experience and broad research interests; discusses finite automata,
context-free languages, and pushdown automata; examines the
concept, universality and limitations of the Turing machine;
investigates computational complexity based on Turing machines and
Boolean circuits, as well as the notion of NP-completeness.
This textbook presents a thorough foundation to the theory of
computation. Combining intuitive descriptions and illustrations
with rigorous arguments and detailed proofs for key topics, the
logically structured discussion guides the reader through the core
concepts of automata and languages, computability, and complexity
of computation. Topics and features: presents a detailed
introduction to the theory of computation, complete with concise
explanations of the mathematical prerequisites; provides
end-of-chapter problems with solutions, in addition to
chapter-opening summaries and numerous examples and definitions
throughout the text; draws upon the author's extensive teaching
experience and broad research interests; discusses finite automata,
context-free languages, and pushdown automata; examines the
concept, universality and limitations of the Turing machine;
investigates computational complexity based on Turing machines and
Boolean circuits, as well as the notion of NP-completeness.
Algorithmic learning theory is mathematics about computer programs
which learn from experience. This involves considerable interaction
between various mathematical disciplines including theory of
computation, statistics, and c- binatorics. There is also
considerable interaction with the practical, empirical ?elds of
machine and statistical learning in which a principal aim is to
predict, from past data about phenomena, useful features of future
data from the same phenomena. The papers in this volume cover a
broad range of topics of current research in the ?eld of
algorithmic learning theory. We have divided the 29 technical,
contributed papers in this volume into eight categories
(corresponding to eight sessions) re?ecting this broad range. The
categories featured are Inductive Inf- ence, Approximate
Optimization Algorithms, Online Sequence Prediction, S- tistical
Analysis of Unlabeled Data, PAC Learning & Boosting,
Statistical - pervisedLearning, LogicBasedLearning,
andQuery&ReinforcementLearning. Below we give a brief overview
of the ?eld, placing each of these topics in the general context of
the ?eld. Formal models of automated learning re?ect various facets
of the wide range of activities that can be viewed as learning. A
?rst dichotomy is between viewing learning as an inde?nite process
and viewing it as a ?nite activity with a de?ned termination.
Inductive Inference models focus on inde?nite learning processes,
requiring only eventual success of the learner to converge to a
satisfactory conclusion.
This book constitutes the refereed proceedings of the 8th
International Workshop on Algorithmic Learning Theory, ALT'97, held
in Sendai, Japan, in October 1997.
The volume presents 26 revised full papers selected from 42
submissions. Also included are three invited papers by leading
researchers. Among the topics addressed are PAC learning, learning
algorithms, inductive learning, inductive inference, learning from
examples, game-theoretical aspects, decision procedures, language
learning, neural algorithms, and various other aspects of
computational learning theory.
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