Grammatical Inference : Learning Automata and Grammars
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| Title: | Grammatical Inference : Learning Automata and Grammars |
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| Description: | 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. |
| Authors: | Colin de la Higuera |
| Resource Type: | eBook. |
| Subjects: | Learning, Linguistics, Automation, Logic, Symbolic and mathematical, Formal languages, Programming languages (Electronic computers), Language and languages |
| Categories: | COMPUTERS / Artificial Intelligence / Natural Language Processing, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition |
| Database: | eBook Collection (EBSCOhost) |
| Abstract: | 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. |
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| ISBN: | 9780521763165 9780511712876 |
