355.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: ECML-2002 call for papers
发信站: 南京大学小百合站 (Sat Apr 13 22:38:38 2002), 站内信件

Call for Papers

                         Le Salon des Refus閟
         When Learning and Mining Efforts Do Not Meet Success

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                           Workshop at the

        13th European Conference on Machine Learning (ECML'02)

                               and the

   6th European Conference on Principles and Practice of Knowledge
                   Discovery in Databases (PKDD'02)

                     19-23 August 2002, Helsinki

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    Motivations

The study of machine learning and data mining methods might fall prey to
the effects of a publication bias that favours sucesses rather than
failures. Most conferences and journals papers focus on positive results
- either explicitly (because this is a clear acceptance criterion) or
implicitly (because researchers have the impression that negative
results would not be accepted).

We believe that this policy of favouring success stories does not
reflect the practice of a field where failures happen regularly. As a
community, we might regard failures as being as informative as
successes, for these are our negative examples... It is our hope that
this workshop, concentrating on unexpected failures, will provide
interesting hints into the current boundaries of our field.

The workshop intends to provide a forum for papers outside the success
stream, which is why we called it Salon des refus閟 <refuses.html>. We
welcome submission of papers reporting on failures that are significant
(as opposed to falling close to the best / average known accuracy) and
unexpected with respect to the current state of art and practice.

The discussion will, if at all possible, provide some hints into the
failure causes regarding the method and/or the problem characteristics.


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    Submissions

In order for the workshop to be accessible to a wide audience within the
community, we welcome papers that report failures of learning and mining
strategies that are already popular and well-known in the community, or
of novel ideas that do not require extensive prior knowledge in a micro
niche of machine learning.

The mode of failure should be put in understandable terms for the
machine learning and data mining practitioner. The causes of failure
must be discussed, and if at all possible, explained. As an alternative,
complementary (further) experiments might be proposed to get further
hints into the failure causes.

The ideal workshop paper should describe an experiment or an idea that
is likely to be repeated by other people, and whose expected outcome is
clear to everybody. The result should fail this expectation.

The main evaluation criterion will be whether it appears to be
worthwhile to record the failure for the community.

Papers should be submitted in electronic form (PDF or PostScript
preferred) to all four organizers, using same format as for ECML/PKDD.
There is no length restriction at submission time. A maximum length will
be set up for the production of the proceedings volume.

All workshops will have the same submission deadlines, which are given
below. In order to guarantee a timely production of the proceedings,
both the camera-ready copy and a Web-version of tutorial notes and
workshop proceedings must be ready by July 12th, 2002.


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   Important Dates

WS paper submission deadline: 24.05.2002

WS paper acceptance notification: 14.06.2002

WS paper camera-ready deadline: 01.07.2002

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   Program Committee

Hilan Bensusan, Universidade de Bras韑ia, Brazil
Peter Flach, University of Bristol, UK
Johannes F黵nkranz, 諪AI, Austria
Christophe Giraud-Carrier, ELCA Informatique, Switzerland
Melanie Hilario, University of Geneva, Switzerland
Alexandros Kalousis, CUI, University of Geneva, Switzerland
Johann Petrak, 諪AI, Austria
Lutz Prechelt, abaXX Technology, Germany
Nada Lavrac, IJS, Slovenia
Mich鑜e Sebag, LRI, Universit