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Topics in grammatical inference

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Topics in grammatical inference/ edited by Jeffrey Heinz, Jose M. Sempere.
other author:	Heinz, Jeffrey.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2016.,
Description:	xvii, 247 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-48395-4
ISBN:	9783662483954

Topics in grammatical inference
Topics in grammatical inference[electronic resource] /edited by Jeffrey Heinz, Jose M. Sempere. - Berlin, Heidelberg :Springer Berlin Heidelberg :2016. - xvii, 247 p. :ill. (some col.), digital ;24 cm.

Introduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences.

This book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.

ISBN: 9783662483954

Standard No.: 10.1007/978-3-662-48395-4doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

Topics in grammatical inference
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300 $a xvii, 247 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences.
520 $a This book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.
650 0 $a Machine learning. $3 561253
650 0 $a Natural language processing (Computer science) $3 641811
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Theory of Computation. $3 669322
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 2 4 $a Computational Linguistics. $3 670080
650 2 4 $a Computational Biology/Bioinformatics. $3 677363
700 1 $a Heinz, Jeffrey. $3 1108796
700 1 $a Sempere, Jose M. $3 1108797
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856 4 0 $u http://dx.doi.org/10.1007/978-3-662-48395-4
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