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Grammatical Inference: Algorithms and Applications

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Cover of 'Grammatical Inference: Algorithms and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Learning and Mathematics
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    Chapter 2 Learning Finite-State Models for Machine Translation
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    Chapter 3 The Omphalos Context-Free Grammar Learning Competition
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    Chapter 4 Mutually Compatible and Incompatible Merges for the Search of the Smallest Consistent DFA
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    Chapter 5 Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptation
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    Chapter 6 Learning Mild Context-Sensitiveness: Toward Understanding Children’s Language Learning
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    Chapter 7 Learnability of Pregroup Grammars
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    Chapter 8 A Markovian Approach to the Induction of Regular String Distributions
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    Chapter 9 Learning Node Selecting Tree Transducer from Completely Annotated Examples
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    Chapter 10 Identifying Clusters from Positive Data
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    Chapter 11 Introducing Domain and Typing Bias in Automata Inference
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    Chapter 12 Analogical Equations in Sequences: Definition and Resolution
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    Chapter 13 Representing Languages by Learnable Rewriting Systems
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    Chapter 14 A Divide-and-Conquer Approach to Acquire Syntactic Categories
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    Chapter 15 Grammatical Inference Using Suffix Trees
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    Chapter 16 Learning Stochastic Finite Automata
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    Chapter 17 Navigation Pattern Discovery Using Grammatical Inference
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    Chapter 18 A Corpus-Driven Context-Free Approximation of Head-Driven Phrase Structure Grammar
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    Chapter 19 Partial Learning Using Link Grammars Data
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    Chapter 20 eg-GRIDS: Context-Free Grammatical Inference from Positive Examples Using Genetic Search
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    Chapter 21 The Boisdale Algorithm – An Induction Method for a Subclass of Unification Grammar from Positive Data
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    Chapter 22 Learning Stochastic Deterministic Regular Languages
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    Chapter 23 Polynomial Time Identification of Strict Deterministic Restricted One-Counter Automata in Some Class from Positive Data
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    Chapter 24 Learning Syntax from Function Words
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    Chapter 25 Running FCRPNI in Efficient Time for Piecewise and Right Piecewise Testable Languages
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    Chapter 26 Extracting Minimum Length Document Type Definitions Is NP-Hard
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    Chapter 27 Learning Distinguishable Linear Grammars from Positive Data
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    Chapter 28 Extending Incremental Learning of Context Free Grammars in Synapse
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    Chapter 29 Identifying Left-Right Deterministic Linear Languages
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    Chapter 30 Efficient Learning of k -Reversible Context-Free Grammars from Positive Structural Examples
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    Chapter 31 An Analysis of Examples and a Search Space for PAC Learning of Simple Deterministic Languages with Membership Queries
Attention for Chapter 26: Extracting Minimum Length Document Type Definitions Is NP-Hard
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Chapter title
Extracting Minimum Length Document Type Definitions Is NP-Hard
Chapter number 26
Book title
Grammatical Inference: Algorithms and Applications
Published by
Springer, Berlin, Heidelberg, October 2004
DOI 10.1007/978-3-540-30195-0_26
Book ISBNs
978-3-54-023410-4, 978-3-54-030195-0
Authors

Henning Fernau, Fernau, Henning

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 1 50%
Unknown 1 50%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 50%
Other 1 50%
Readers by discipline Count As %
Computer Science 2 100%

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