Colin de la Higuera – författare

The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.

Table of Contents: List of Figures / List of Tables / Preface / Studying Learning / Formal Learning / Learning Regular Languages / Learning Non-Regular Languages / Lessons Learned and Open Problems / Bibliography / Author Biographies

This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996.The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language and pattern recognition, inference and stochastic models, incremental methods and inductive logic programming, and operational issues.

This book facilitates understanding of how artificial intelligence (AI) aids and integrates digital transformation (DT) in education institutions worldwide in various scenarios: learning environments (learning innovation, learning management systems, data and analytics), emerging education trends (business trends, strategic technologies), administrative systems (recruit, retain, advance, enterprise business capabilities, student information systems), and digital strategy execution (business models and opportunities, strategic planning and governance). It serves as a reference for university lecturers, schoolteachers, policymakers, and international organizations, who will find in its various chapters practical recommendations and discoveries from practice, ready to be implemented in their contexts.Chapters 1, 2, 7 and 9 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.