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Topics in grammatical inference

Heinz, Jeffrey.

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Topics in grammatical inference

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Topics in grammatical inference/ edited by Jeffrey Heinz, Jose M. Sempere.
其他作者:	Heinz, Jeffrey.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2016.,
面頁冊數:	xvii, 247 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	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:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

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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.
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