Grammatical Inference: Theoretical Results and Applications
Grammatical Inference: Theoretical Results and Applications

Author: José Sempere

Publisher: Springer Science & Business Media

Published: 2010-09-03

Total Pages: 328

ISBN-13: 3642154875

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This book constitutes the refereed proceedings of the 10th International Colloquium on Grammatical Inference, ICGI 2010, held in Valencia, Spain, in September 2010. The 18 revised full papers and 14 revised short papers presented were carefully reviewed and selected from numerous submissions. The topics of the papers presented vary from theoretical results about the learning of different formal language classes (regular, context-free, context-sensitive, etc.) to application papers on bioinformatics, language modelling or software engineering. Furthermore there are two invited papers on the topics grammatical inference and games and molecules, languages, and automata.

Grammatical Inference: Algorithms and Applications
Grammatical Inference: Algorithms and Applications

Author: Arlindo L. Oliveira

Publisher: Springer

Published: 2004-02-13

Total Pages: 321

ISBN-13: 3540452575

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This book constitutes the refereed proceedings of the 5th International Colloquium on Grammatical Inference, ICGI 2000, held in Lisbon, Portugal in September 2000. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.

State of the Art on Grammatical Inference Using Evolutionary Method
State of the Art on Grammatical Inference Using Evolutionary Method

Author: Hari Mohan Pandey

Publisher: Academic Press

Published: 2021-11-13

Total Pages: 230

ISBN-13: 0128221542

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State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference. - Discusses and summarizes the latest developments in Grammatical Inference, with a focus on Evolutionary Methods - Provides an understanding of premature convergence as well as genetic algorithms - Presents a performance analysis of genetic algorithms as well as a complete look into the wide range of applications of Grammatical Inference methods - Demonstrates how to develop a robust experimental environment to conduct experiments using evolutionary methods and algorithms

Grammatical Inference: Algorithms and Applications
Grammatical Inference: Algorithms and Applications

Author: Georgios Paliouras

Publisher: Springer Science & Business Media

Published: 2004-10-05

Total Pages: 300

ISBN-13: 3540234101

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This book constitutes the refereed proceedings of the 7th International Colloquium on Grammatical Inference, ICGI 2004, held in Athens, Greece in October 2004. The 20 revised full papers and 8 revised poster papers presented together with 3 invited contributions were carefully reviewed and selected from 45 submissions. The topics of the papers presented range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to estimations of probabilistic grammars.

Topics in Grammatical Inference
Topics in Grammatical Inference

Author: Jeffrey Heinz

Publisher: Springer

Published: 2016-05-04

Total Pages: 258

ISBN-13: 3662483955

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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.

Grammatical Inference: Algorithms and Applications
Grammatical Inference: Algorithms and Applications

Author: Alexander Clark

Publisher: Springer Science & Business Media

Published: 2008-09-11

Total Pages: 314

ISBN-13: 3540880089

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This book constitutes the refereed proceedings of the 9th International Colloquium on Grammatical Inference, ICGI 2008, held in Saint-Malo, France, in September 2008. The 21 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 36 submissions. The topics of the papers presented vary from theoretical results of learning algorithms to innovative applications of grammatical inference, and from learning several interesting classes of formal grammars to applications to natural language processing.

Grammatical Inference: Algorithms and Applications
Grammatical Inference: Algorithms and Applications

Author: Yasibumi Sakaibara

Publisher: Springer

Published: 2006-11-28

Total Pages: 370

ISBN-13: 3540452656

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This book constitutes the refereed proceedings of the 8th International Colloquium on Grammatical Inference, ICGI 2006. The book presents 25 revised full papers and 8 revised short papers together with 2 invited contributions, carefully reviewed and selected. The topics discussed range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to applications to natural language processing.

Grammatical Inference
Grammatical Inference

Author: Wojciech Wieczorek

Publisher: Springer

Published: 2016-10-25

Total Pages: 152

ISBN-13: 3319468014

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This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. divThough the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>

Empiricism and Language Learnability
Empiricism and Language Learnability

Author: Nick Chater

Publisher: OUP Oxford

Published: 2015-07-09

Total Pages: 217

ISBN-13: 0191053597

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This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. For many years, the empiricist approach has been taken to be unfeasible on practical and theoretical grounds. In the book, the authors present a variety of precisely specified mathematical and computational results that show that empiricist approaches can form a viable solution to the problem of language acquisition. It assumes limited technical background and explains the fundamental principles of probability, grammatical description and learning theory in an accessible and non-technical way. Different chapters address the problem of language acquisition using different assumptions: looking at the methodology of linguistic analysis using simplicity based criteria, using computational experiments on real corpora, using theoretical analysis using probabilistic learning theory, and looking at the computational problems involved in learning richly structured grammars. Written by four researchers in the full range of relevant fields: linguistics (John Goldsmith), psychology (Nick Chater), computer science (Alex Clark), and cognitive science (Amy Perfors), the book sheds light on the central problems of learnability and language, and traces their implications for key questions of theoretical linguistics and the study of language acquisition.

Model Selection and Error Estimation in a Nutshell
Model Selection and Error Estimation in a Nutshell

Author: Luca Oneto

Publisher: Springer

Published: 2019-07-17

Total Pages: 135

ISBN-13: 3030243591

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How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.