2016, ISBN: 3662483939
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2016, ISBN: 3662483939
[EAN: 9783662483930], Neubuch, [SC: 0.0], [PU: Springer Berlin Heidelberg], INTELLIGENZ / KÜNSTLICHE INTELLIGENZ; KI; - AI; AUTOMATATHEORY; FORMALLANGUAGES; GRAMMATICALINFERENCE; GRAPHGRA… Mehr…
ISBN: 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 th… Mehr…
2016
ISBN: 3662483939
This book explains advanced theoretical andapplication-related issues in grammatical inference, a research area inside theinductive inference paradigm for machine learning. The first thre… Mehr…
ISBN: 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 th… Mehr…
2016, ISBN: 9783662483930
*Topics in Grammatical Inference* - 1st ed. 2016 / gebundene Ausgabe für 106.99 € / Aus dem Bereich: Bücher, Ratgeber, Computer & Internet Medien > Bücher nein Buch (gebunden) Hardcover;N… Mehr…
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Detailangaben zum Buch - Topics in Grammatical Inference
EAN (ISBN-13): 9783662483930
ISBN (ISBN-10): 3662483939
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2016
Herausgeber: Springer Berlin Heidelberg
Buch in der Datenbank seit 2016-06-02T22:33:21+02:00 (Zurich)
Detailseite zuletzt geändert am 2024-01-20T11:58:15+01:00 (Zurich)
ISBN/EAN: 3662483939
ISBN - alternative Schreibweisen:
3-662-48393-9, 978-3-662-48393-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: sempé, semper, sempe, von heinz
Titel des Buches: topic, grammatica, grammatic, grammatical, topics gramm
Daten vom Verlag:
Autor/in: Jeffrey Heinz; José M. Sempere
Titel: Topics in Grammatical Inference
Verlag: Springer; Springer Berlin
247 Seiten
Erscheinungsjahr: 2016-05-13
Berlin; Heidelberg; DE
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XVII, 247 p. 56 illus., 7 illus. in color.
BB; Hardcover, Softcover / Informatik, EDV/Informatik; Theoretische Informatik; Verstehen; Informatik; Automata Theory; Formal Languages; Grammatical Inference; Graph Grammars; Machine Learning; Computational Linguistics; Biosequences; Theory of Computation; Artificial Intelligence; Computational Linguistics; Computational and Systems Biology; Künstliche Intelligenz; Computerlinguistik und Korpuslinguistik; DV-gestützte Biologie/Bioinformatik; EA; BC
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.
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.
Contributing authors among leading researchers in this Valuable for researchers and graduate students in theoretical computer science, computational linguistics, bioinformatics, and cognitive psychology Topics of foundational interest with mature and established results, algorithms and conclusions Includes supplementary material: sn.pub/extras
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