Topics in Grammatical Inference
Language: English
Published by Springer, 2016
- Hardcover
- New

Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
AbeBooks seller since April 6, 2009
Condition: New
US$ 134.95
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- Title
- Topics in Grammatical Inference
- Author
- Heinz, Jeffrey (EDT); Sempere, José M. (EDT)
- Publisher
- Springer
- Publication year
- 2016
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3662483939
- ISBN 13
- 9783662483930
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
GreatBookPrices
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