Topics in Grammatical Inference

José M. Sempere

ISBN 10: 3662569205 / ISBN 13: 9783662569207

Editore: Springer Berlin Heidelberg Mai 2018, 2018

Riguardo questo articolo

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 9783662569207

Dati bibliografici

• Titolo: Topics in Grammatical Inference
• Editore: Springer Berlin Heidelberg Mai 2018
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3662569205
• ISBN 13: 9783662569207
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 15 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 15 millimetri di profondità
• Peso: 411 grammi

Descrizione articolo

Riassunto

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.