||[ E-CFP ] CFP of the MLJ special issue on Grammatical Inference
||26 Jun 2012
||01 Dec 2012
CALL FOR PAPERS
Special Issue on Grammatical
Machine Learning Journal
Grammatical inference studies the problem of how a grammar can be
reliably and automatically inferred from information about the behavior
of the system the grammar characterizes. Generative grammars are used to
model a range of behaviors in fields such as bioinformatics, psychology,
linguistics, natural language processing, software engineering, and many
Research in grammatical inference continually appears in conferences,
including the biennial International Conference of Grammatical Inference
(ICGI), and journals, and is the subject of a recent book (de la Higuera
2010). The purpose of this special issue is to present the best,
cutting-edge research on grammatical inference to the readership of the
Machine Learning Journal.
We invite high quality submissions from researchers in all areas of
grammatical inference, including, but not limited to, the following areas:
* Theoretical aspects of grammatical inference: learning paradigms,
learnability results, complexity of learning. Efficient learning
algorithms for language classes inside and outside the Chomsky
hierarchy. Learning tree and graph grammars. Learning distributions
over strings, trees or graphs.
* Theoretical and experimental analysis of different approaches to
grammar induction, including artificial neural networks, statistical
methods, symbolic methods, information-theoretic approaches, minimum
description length, complexity-theoretic approaches, heuristic
* Novel approaches to grammatical inference: Induction by DNA
computing or quantum computing, evolutionary approaches, new
representation spaces, etc.
* Successful applications of grammatical inference to tasks in
natural language processing, bioinformatics, machine translation,
pattern recognition, language acquisition, software engineering,
computational linguistics, spam and malware detection, cognitive
psychology, robotics etc.
Authors are encouraged to submit high-quality, original work that has
neither appeared in, nor is under consideration by, other journals.
Springer offers authors, editors and reviewers of Machine Learning a
web-enabled online manuscript submission and review system, giving
authors the ability to track the review process of their manuscript.
Manuscripts should be submitted to: http://MACH.edmgr.com. This online
system offers easy and straightforward log-in and submission procedures,
and supports a wide range of submission file formats. When submitting
please be sure to choose the manuscript type, "Grammatical Inference."
. Paper submission deadline: December 1, 2012
. Notification of acceptance: February 1, 2013
. Final manuscript: June 1, 2013
Jeffrey Heinz (University of Delaware, heinz_(at)_udel.edu)
Colin de la Higuera (University of Nantes, cdlh_(at)_univ-nantes.fr)
Tim Oates (University of Maryland, oates_(at)_cs.umbc.edu)
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