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This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences.
The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Dalla quarta di copertina
This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences.
The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Topics in Grammatical Inference
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Springer Berlin Heidelberg Mai 2016, 2016
ISBN 10: 3662483939
ISBN 13: 9783662483930
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first three chapters ofthe book deal with issues regarding theoretical learning frameworks; the nextfour chapters focus on the main classes of formal languages according toChomsky's hierarchy, in particular regular and context-free languages; and thefinal chapter addresses the processing of biosequences.The topics chosen are of foundational interest withrelatively mature and established results, algorithms and conclusions. The bookwill be of value to researchers and graduate students in areas such astheoretical computer science, machine learning, computational linguistics, bioinformatics,and cognitive psychology who are engaged with the study of learning, especiallyof the structure underlying the concept to be learned. Some knowledge ofmathematics and theoretical computer science, including formal language theory,automata theory, formal grammars, and algorithmics, is a prerequisite forreading this book. 268 pp. Englisch. Codice articolo 9783662483930
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Buch. Condizione: Neu. Topics in Grammatical Inference | José M. Sempere (u. a.) | Buch | xvii | Englisch | 2016 | Springer-Verlag GmbH | EAN 9783662483930 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 104334650
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