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文法推斷:算法與應(yīng)用

文法推斷:算法與應(yīng)用

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作 者: Pieter Adriaans 著
出版社: 湖南文藝出版社
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ISBN: 9783540442394 出版時(shí)間: 2002-12-01 包裝: 平裝
開本: 頁數(shù): 字?jǐn)?shù):  

內(nèi)容簡(jiǎn)介

  This book constitutes the refereed proceedings of the 6th International Colloquium on Grammatical Inference, ICGI 2002, held in Amsterdam, The Netherlands in September 2002.The 28 revised full papers presented together with 7 software de*ions were carefully reviewed and selected from 48 submissions. The papers address issues in machine learning, automata, theoretical computer science, computational linguistics, and grammar systems as well as applications in fields like natural language processing, pattern recognition, computational biology, information retrieval, text processing, and data compression.

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圖書目錄

Contributions
Inference of Sequential Association Rules Guided by Context-Free Grammars
 PCFG Learning by Nonterminal Partition Search
 Inferring Subclasses of Regular Languages Faster Using RPNI and Forbidden Configurations
 Beyond EDSM
 Consistent Identification in the Limit of Rigid Grammars from Strings Is NP-hard
 Some Classes of Regular Languages Identifiable in the Limit from Positive Data
 Learning Probabilistic Residual Finite State Automata
 Fragmentation: Enhancing Identifiability
 On Limit Points for Some Variants of Rigid Lambek Grammars
 Generalized Stochastic Tree Automata for Multi-relational Data Mining
 On Sufficient Conditions to Identify Classes of Grammars from Polynomial Time and Data
 Stochastic Grammatical Inference with Multinomial Tests
 Learning Languages with Help
 Incremental Learning of Context Free Grammars
 Estimating Grammar Parameters Using Bounded Memory
 Stochastic k-testable Tree Languages and Applications
 Fast Learning from Strings of 2-Letter Rigid Grammars
 Learning Locally Testable Even Linear Languages from Positive Data
 Inferring Attribute Grammars with Structured Data for Natural Language Processing
 A PAC Learnability of Simple Deterministic Languages
 On the Learnability of Hidden Markov Models
 Shallow Parsing Using Probabilistic Grammatical Inference
 Learning of Regular Bi-w Languages
Software Descriptions
 The EMILE 4.1 Grammar Induction Toolbox
 Software for Analysing Recurrent Neural Nets That Learn to Predict Non-regular Languages
 A Framework for Inductive Learning of Typed-Unification Grammars
 A Tool for Language Learning Based on Categorial Grammars and Semantic Information
 ‘NAIL’: Artificial Intelligence Software for Learning Natural Language
……
Autor Index

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