Results 241 to 250 of about 9,057 (292)

A Proposal for Contextual Grammatical Inference

Lecture Notes in Computer Science, 2015
Grammatical Inference deals with the learning of formal languages from data. Research in this field has mainly reduced the problem of language learning to syntax learning. Taking into account that the theoretical results obtained in Grammatical Inference show that learning formal languages only from syntax is generally hard, in this paper we propose to
Leonor Becerra-Bonache, Becerra-Bonache Leonor, Jacquenet François   +2 more
exaly   +2 more sources

Grammatical Inference: Introduction and Survey - Part I

IEEE Transactions on Systems, Man, and Cybernetics, 1975
Inference of high-dimensional grammars is discussed. Specifically, techniques for inferring tree grammars are briefly presented. The problem of inferring a stochastic grammar to model the behavior of an information source is also introduced and techniques for carrying out the inference process are presented for a class of stochastic finite-state and ...
Taylor L Booth
exaly   +8 more sources

Evolutionary Grammatical Inference

, 2009
Grammatical Inference (also known as grammar induction) is the problem of learning a grammar for a language from a set of examples. In a broad sense, some data is presented to the learner that should return a grammar capable of explaining to some extent the input data.
Ernesto Rodrigues, Heitor Silvério Lopes   +1 more
openaire   +2 more sources

Stochastic Grammatical Inference with Multinomial Tests

, 2002
We present a new statistical framework for stochastic grammatical inference algorithms based on a state merging strategy. We propose to use multinomial statistical tests to decide which states should be merged. This approach has three main advantages. First, since it is not based on asymptotic results, small sample case can be specifically dealt with ...
Christopher Kermorvant, Pierre Dupont
openaire   +2 more sources

Using Grammatical Inference

, 2004
In the recent years, the amount of available textual documents has drastically increased, specially thanks to the electronic communication means like the WEB. Hence, technics allowing to structure, classify and recognize parts of this huge data base will allow a better use the available informations.
Saidi, Alexandre
core   +4 more sources

Grammatical Inference

, 2017
This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed.
Wieczorek, Wojciech
openaire   +2 more sources

Grammatical Inference and Games: Extended Abstract

, 2010
This paper discusses the potential synergy between research in grammatical inference and research in artificial intelligence applied to games. There are two aspects to this: the potential as a rich source of challenging and engaging test problems, and the potential for real applications.
Lucas, Simon M, Simon M. Lucas
openaire   +2 more sources

Grammatical inference by Hill Climbing

Information Sciences, 1976
A cost function is developed, based on information-theoretic concepts, that measures the complexity of a stochastic context-free grammar, as well as the discrepancy between its language and a given stochastic language sample. This function is used to guide a search procedure that finds simple grammars whose languages are good fits to a sample ...
Craig M. Cook, Azriel Rosenfeld, Alan R. Aronson   +2 more
openaire   +1 more source

Grammatical Inference as Class Discrimination

2010
Grammatical inference is typically defined as the task of finding a compact representation of a language given a subset of sample sequences from that language. Many different aspects, paradigms and settings can be investigated, leading to different proofs of language learnability or practical systems.
Menno van Zaanen, Tanja Gaustad
openaire   +2 more sources

Grammatical inference of colonies

1997
A concept of accepting colonies is introduced. A hybrid connectionist-symbolic architecture (“neural pushdown automaton”) for inference of colonies based on presentation of positive and negative examples of strings is then described, together with an algorithm for extracting a colony from trained neural network.
Petr Sosík, Leos Stýbnar
openaire   +1 more source