Subject: call for papers
From: [email-masked] (Charles Taylor) (by way of [email-masked] (Ramon Lopez de Mantaras))
To: [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked], [email-masked],
Date: Mon, 02 Dec 1996 16:26:19 +0100

Please could you forward this to the MLnet group.

**************************************************************************
                           CALL FOR PAPERS

                    MLNET FAMILIARIZATION WORKSHOP

                         26th April 1997

                  LEARNING IN DYNAMICALLY CHANGING DOMAINS:
                THEORY REVISION AND CONTEXT DEPENDENCE ISSUES.
**************************************************************************

                   Up-to-date information will be kept at:
                http://www.amsta.leeds.ac.uk/statistics/ecml97/dyn.htm

**********
BACKGROUND
**********

In traditional Machine Learning the available examples (the training data)
are usually used to learn a concept. In many practical
situations in which the environment changes, this procedure ceases to work.
Concerning the case of supervised learning, generally speaking, the
application of a discrimination algorithm to classify new, unseen examples
will be problematic if one of the following events occurs after the
``learning phase'':
* The number of attributes changes
* The number of attributes remains the same but the interpretation of
  the records of the datasets changes over  time
* A description of a concept (class) exists but there are additional databases
  relating to the given concept (class) that may modify (refine) the existing
  knowledge base

In the case of  supervised learning (classification or prediction),
the extracted rules or dependencies can be no longer valid over
time due to different changes which can occur. However, this statement is
probably valid for unsupervised and reinforcement learning as well.

**************
THE CHALLENGE:
**************

There have been some attempts in the literature to address the problem of
structural change in concepts and the dynamic aspects of data in Machine
Learning. But most of the ML-algorithms can still not deal with this problem.
Research in this direction has very important practical implications because
structural change in concepts occurs often in the real-world domain.
By considering this issue  Machine Learning has a better chance of acceptance
in industry and commerce.

There are many contributions that Statisticians have (already) made to this
field, but their communication is hampered by a different terminology, for
example:
           ML                                      Statistics
           --                                      ----------
Context Learning                        Parameter estimation in
multivariate regression
Dynamic Learning                        Structure Change (econometrics)
Theory Revision, Knowledge Integration  Hypothesis testing

So, the question is:

   Have the Statisticians done everything; What is the challenge for ML?

***************
RELEVANT TOPICS:
***************

The ideal papers would be those which discuss (preferably two or more)
learning aspects (from machine learning, Statistics and  Neural Nets)  in the
following areas:
* Structural change
* CUSUM tests/ Quality control
* Dynamic learning, dynamic models
* Incremental learning, Sequential learning
* Context learning
* Theory revision
* Knowledge Integration

****************
AIMS AND PROGRAM
****************

It is the aim of this workshop to :

* Bring ML and statistics researchers together
* Discuss the state of art of structural change in concepts. This discussion
  should not involve only symbolic ML but, specially, the statistical ML as
  well. Relevant contributions using Neural Networks are also welcome.
* Discuss the direction for further research in the structural change in
  concepts bearing in mind that the main goal is solving real-world problems.

   The  program (April 26) will include invited talks, presentations of
accepted papers (both verbal and poster presentations). All of the
contributions will be summarized by a member of the organizing committee in
a talk. The contributions (including the invited talks) will be distributed
as workshop notes.

***********
ORGANIZERS
***********
Gholamreza Nakhaeizadeh  (Daimler-Benz, Germany)
Charles Taylor (University of Leeds, UK)
Ivan Bruha< (McMaster University, Canada)

*********************
SUBMISSION OF PAPERS:
*********************

Two kinds of submissions are solicited: full papers describing substantial
completed research or applications, and poster papers reporting on work in
progress. Submissions must be clearly marked as one of these two kinds.  The
program committee may decide to move accepted contributions from the full
paper to the poster category.

The size limit for submissions is 12 pages for full papers, 5 pages for poster
papers (excluding title page and bibliography, but including all tables and
figures).

It is hoped that selected contributions will be subsequently published in an
integrated volume. Submitted papers should preferably be formatted according
to the LNAI guidelines (LaTeX style files are available at
http://is.vse.cz/ecml97/styles.htm). Authors are encouraged to make their
papers available in advance (by anonymous ftp or a URL site) so that wider
discussion is possible.  In future the above page will provide links to such
papers.

 A separate title page must contain the title of the paper, the names and
addresses of all authors, up to three keywords, and an abstract of max. 200
words. The full address, including phone, fax, and e-mail, must be given for
the first author (or the contact person).

 The following items must be submitted by February 15, 1997:
Either a camera-ready copy of the paper or a PS (uuencoded) file, together
with an electronic version of the titlepage only (plain ASCII).
 Send submissions, enquiries, etc. to:
        Gholamreza Nakhaeizadeh (ECML-97)
        Daimler Benz AG
        Research and Technology
        Postfach 2360
        D-89013 Ulm
        Germany
        e-mail:  [email-masked]

Papers will be evaluated with respect to relevance, technical soundness,
significance, originality, and clarity. Papers reporting on real-world
applications will be evaluated according to special criteria.

*************************************
REGISTRATION AND FURTHER INFORMATION:
*************************************
The workshops will be open to anyone. Participants who are not members
of MLnet pay a fee to cover the marginal costs of the workshop. The fee
is yet to be determined. MLnet will pay the organisational costs for
its members.

MLnet will arrange travel bursaries for its members to take part in the
workshops

For information about paper submission and program, contact the program
chair. For information about local arrangements, registration forms, etc.
contact the
local organizers at [email-masked]

****************
IMPORTANT DATES:
****************
  Submission deadline:          15 February 1996
  Notification of acceptance:   8  March 1997
  Camera ready copy:            1  April 1997
  Workshop:                     26 April 1997