Journal ArticleDOI
Evolutionary induction of stochastic context free grammars
Bill Keller,Rudi Lutz +1 more
TLDR
This paper describes an evolutionary approach to the problem of inferring stochastic context-free grammars from finite language samples that employs a distributed, steady-state genetic algorithm, with a fitness function incorporating a prior over the space of possible Grammars.About:
This article is published in Pattern Recognition.The article was published on 2005-09-01. It has received 28 citations till now. The article focuses on the topics: L-attributed grammar & Context-sensitive grammar.read more
Citations
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Journal ArticleDOI
A stochastic context free grammar based framework for analysis of protein sequences.
TL;DR: A new Stochastic Context Free Grammar based framework has been introduced allowing the production of binding site descriptors for analysis of protein sequences and suggests that this system may be particularly suited to deal with patterns shared by non-homologous proteins.
Journal ArticleDOI
A memetic grammar inference algorithm for language learning
TL;DR: An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described and a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.
Journal ArticleDOI
Learning deterministic context free grammars: The Omphalos competition
TL;DR: A context-free grammatical inference algorithm operating on positive data only is described, which integrates an information theoretic constituent likelihood measure together with more traditional heuristics based on substitutability and frequency.
Journal ArticleDOI
How evolutionary algorithms are applied to statistical natural language processing
TL;DR: A survey of many works which apply EAs to different NLP problems, including syntactic and semantic analysis, grammar induction, summaries and text generation, document clustering and machine translation is presented.
Proceedings ArticleDOI
Learning Dynamic Event Descriptions in Image Sequences
TL;DR: This work introduces a novel method for representing and classifying events in video sequences using reversible context-free grammars and demonstrates the efficacy of the learning algorithm and the event detection method applied to traffic video sequences.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
The Mathematical Theory of Communication
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.