13.3235, Calls: Pattern Recognition/Authomatic Learning
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Mon Dec 9 20:07:02 UTC 2002
LINGUIST List: Vol-13-3235. Mon Dec 9 2002. ISSN: 1068-4875.
Subject: 13.3235, Calls: Pattern Recognition/Authomatic Learning
Moderators: Anthony Aristar, Wayne State U.<aristar at linguistlist.org>
Helen Dry, Eastern Michigan U. <hdry at linguistlist.org>
Reviews (reviews at linguistlist.org):
Simin Karimi, U. of Arizona
Terence Langendoen, U. of Arizona
Consulting Editor:
Andrew Carnie, U. of Arizona <carnie at linguistlist.org>
Editors (linguist at linguistlist.org):
Karen Milligan, WSU Naomi Ogasawara, Arizona U.
James Yuells, EMU Marie Klopfenstein, WSU
Michael Appleby, EMU Heather Taylor, EMU
Ljuba Veselinova, Stockholm U. Richard John Harvey, EMU
Dina Kapetangianni, EMU Renee Galvis, WSU
Karolina Owczarzak, EMU Anita Huang, EMU
Tomoko Okuno, EMU Steve Moran, EMU
Lakshmi Narayanan, EMU Sarah Murray, WSU
Marisa Ferrara, EMU
Software: Gayathri Sriram, E. Michigan U. <gayatri at linguistlist.org>
Zhenwei Chen, E. Michigan U. <chen at linguistlist.org>
Prashant Nagaraja, E. Michigan U. <prashant at linguistlist.org>
Home Page: http://linguistlist.org/
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Editor for this issue: Karolina Owczarzak <karolina at linguistlist.org>
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1)
Date: Mon, 9 Dec 2002 13:02:49 +0100 (CET)
From: Menno van Zaanen <mvzaanen at science.uva.nl>
Subject: Journal of the Pattern Recognition Society
2)
Date: Mon, 9 Dec 2002 13:36:55 +0100
From: ALAF 2003 - Workshop <alaf03 at ckl.ms.mff.cuni.cz>
Subject: Authomatic Learning Methods, Vienna Austria
-------------------------------- Message 1 -------------------------------
Date: Mon, 9 Dec 2002 13:02:49 +0100 (CET)
From: Menno van Zaanen <mvzaanen at science.uva.nl>
Subject: Journal of the Pattern Recognition Society
***New Deadline: January 3, 2003***
CALL FOR PAPERS
Pattern Recognition
(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):
Theory:
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
Transduction learning
Applications:
Image processing and computer vision
Biosequence analysis and prediction
Speech and natural language processing
Data mining/information retrieval
Optical character recognition
Submission Procedure:
Only electronic (ftp) submission will be accepted. Instructions for
submission of papers can be found 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.
Deadlines:
Manuscript Submission: January 3, 2003 **** NEW DEADLINE ***
Notification of Acceptance: April 16, 2003
Final Manuscript Due: June 16, 2003
Publication Date: April 2004
Guest Editor:
Mitra Basu , The City College of CUNY, New York, U.S.A.
basu at ccny.cuny.edu
-------------------------------- Message 2 -------------------------------
Date: Mon, 9 Dec 2002 13:36:55 +0100
From: ALAF 2003 - Workshop <alaf03 at ckl.ms.mff.cuni.cz>
Subject: Authomatic Learning Methods, Vienna Austria
***********************************************************
*** FIRST CALL for PAPERS ***
*** ***
***********************************************************
ESSLLI'03 ( http://www.logic.at/esslli03/ )
Workshop on
"Adaptation of Automatic Learning Methods for
Analytical and Inflectional Languages"
(ALAF'03)
August 18-22, 2003
Vienna, Austria
***********************************************************
WORKSHOP WEBPAGE http://ckl.mff.cuni.cz/~alaf03
***********************************************************
***********************************************************
* IMPORTANT DATES *
* *
***********************************************************
***********************************************************
* March 14, 2003 Submission Deadline *
* (Extended Abstracts, Homework Proposals)*
* *
* April 14, 2003 Notification of Acceptance *
* *
***********************************************************
*** TOPICS of INTEREST
Automatic (machine) learning approaches to any NLP task became a rich
area with a variety of methodologies. During the last years, its
development made significant progress in the direction of presenting
new methods and, at the same time, their modifications. These
modifications are of different nature and dependent on the language
under consideration. The aim of the workshop is to present and
evaluate various modifications of the automatic learning methods
originally developed for English and declared as language
independent. We are especially interested in automatic learning
methods for the problems of morphological tagging and parsing across
languages with high level of inflection. Further, we encourage
quantitative and qualitative comparison/evaluation studies across
languages on the inputs and the outputs of the mentioned
procedures.
The workshop encourages reports of work on:
1. Summarization of morphological and syntactic features relevant for
various automatic learning procedures.
2. Tendencies of improvement of the automatic learning methods.
Presentation of implemented modifications and their cross language
evaluation.
3. New/Latest algorithms for automatic learning.
4. Hybrid approaches (Although, there are trials to apply hybrid
approaches, it seems that the true key of how to combine the various
parts has still not been found and lies mainly in the success of
analyzing the errors of each single component. Studies which present
the connection elements for a successful combination of diverse
approaches are invited.)
In addition to the regular papers, we want to encourage all workshop
participants to make the workshop more interactive; in other words
** Let's enjoy and Don't be afraid of HOMEWORK
Each workshop participant will have the possibility to assign
a homework - he/she becomes a "teacher". This homework should be close
to the topics of our workshop; the choice of either theoretical or
experimental (practical) character of the homework is up to the
"teacher".
Accepted homework assignments (proposals) will be posted on
the workshop webpage not later than April 24, 2003.
The organizers will contact the ESSLLI/Workshop participants
and ask them to sign for the homework assignements. The current
status of each homework will be posted on our web page, allowing the
"teachers" to follow the completition of their homework assignments
and have access to the list of contacts of those who signed for the
homework.
Upon completition (each homework should be completed *** a
WEEK *** before the start of the Summer School), the results should be
submitted to the Workshop organizers. Those will collect them and
pass them to the "teachers" a week before the Workshop starts. The
"teachers" will be asked to evaluate them and to prepare a summary of
each homework. Summaries of the outcomes will be presented during the
final session by the "teachers". The accepted homework proposals will
be included (as an appendix) into the workshop proceedings while the
summaries will be posted on the workshop webpage after the Workshop.
Each homework should be completed before the start of the
Summer School.
* How to motivate you? DON'T LET IT BE!
The most interesting homework assignment and the most
productive homework will be awarded during the final session. We are
looking forward to the award ceremony!
FOR MORE DETAILS ON THE HOMEWORKS PROPOSALS
AND ON THE WORKSHOP IN GENERAL
FOLLOW THE WORKSHOP WEBPAGE
http://ckl.mff.cuni.cz/~alaf03
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