Apologies for Multiple Copies.
CALL FOR PAPERS
(The Journal of the Pattern Recognition Society)
Special Issue on Grammatical Inference Techniques & Applications
This Special Issue will be published in April, 2004 to commemorate and honor
the memory of Late Professor K. S. Fu. Grammatical Inference (GI) is a
collection of methodologies for learning grammars from training data. The most
traditional field of application of GI has been syntactic pattern recognition.
In the recent past, however, concerted efforts from diverse disciplines to
find tractable inference techniques have added new dimensions and opened up
unchartered territories. Applications of GI in more nontraditional fields
include Gene Analysis, Sequence Prediction, Cryptography and Information
Retrieval. Development of algorithms for GI has evolved over the years from
dealing with only positive training samples to more fundamental efforts that
try to circumvent the lack of negative samples.. This idea is pursued in
stochastic grammars and languages which attempt to overcome absence of
negative samples by gathering statistical information from available positive
samples. Also within the framework of information theory, probability
estimation technique for Hidden Markov Model known as Backward-Forward and for
Context-Free language, the Inside-Outside algorithm are focal point of
investigations in stochastic grammar field. Techniques that use intelligent
search to infer the rules of grammar are showing considerable promise.
Recently, there has been a surge of activities dealing with specialized
neural network architecture and dedicated learning algorithms to approach GI
problems. In more customary track, research in learning classes of
transducers continue to arouse interests in GI community. Close
interaction/collaboration between different disciplines and availability of
powerful computers are fueling novel research efforts in GI.
The objective of the Special Issue is to present the current status of this
topic through the works of researchers in different disciplines. Original and
tutorial papers are solicited that address theoretical and practical issues
on this theme. Topics of interest include (but are not limited to):
Neural network framework and learning algorithms geared to GI
GI via heuristic and genetic search
Inference mechanisms for stochastic grammars/languages
Algebraic methods for identification of languages
Image processing and computer vision
Biosequence analysis and prediction
Speech and natural language processing
Data mining/information retrieval
Optical character recognition
Only electronic (ftp) submission will be accepted. Instructions for submission
of papers will be posted on November 10 at the guest editor's web site
(http://www-ee.ccny.cuny.edu/basu) . All submitted papers will be reviewed
according to guidelines and standards of Pattern Recognition.
Manuscript Submission: December 10, 2002
Notification of Acceptance: April 16, 2003
Final Manuscript Due: June 16, 2003
Publication Date: April 2004
Mitra Basu , The City College of CUNY, New York, U.S.A.
| Menno van Zaanen | "Let him not vow to walk in the dark,
| email@example.com | who has not seen the nightfall."
| http://www.science.uva.nl/~mvzaanen | -Elrond
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