Connectionists: CFP: Incremental Topological Learning Models and Dimensional Reduction

[Akira Imada]

CFP:
ICNNAI-2010 Special Session:
Incremental Topological Learning Models and Dimensional Reduction

See also https://sites.google.com/site/itlmdm
or
http://icnnai.bstu.by/special-session-1.htm


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Submissions Due:  April  4,  2010
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1 - 4 June, 2010
Brest State Technical University
Belarus

http://icnnai.bstu.by/icnnai-2010.html

SCOPE
Incremental Learning is a subfield of the Artificial Intelligence that deals
with data flow. The key hypothesis is that the algorithms are able to learn
data from a data subset and then to re-learn with new unlabeled data. At the
end of the learning, one of the problems is the clustering analysis and
visualization of the results. The topological learning is one of the most
known technique that allows clustering and visualization simultaneously. At
the end of the topographic learning, "similar'' data will be collect in
clusters, which correspond to the sets of similar observations. These
clusters can be represented by more concise information than the brutal
listing of their patterns, such as their gravity center or different
statistical moments. As expected, this information is easier to manipulate
than the original data points.

Dimensionality reduction is another major challenge in the domain of
unsupervised learning which deals with the transformation of a high
dimensional dataset into a low dimensional space, while retaining most of
the useful structure in the original data, retaining only relevant features
and observations. Dimensionality reduction can be achieved by using a
clustering technique to reduce the number of observations or a features
selection approach to reduce the features space.

This session would solicit theoretical and applicative research papers
including but not limited to the following topics :

  - Supervised/Unsupervised Topological Learning;
  - Self-Organization
    (based on artificial neural networks, but not limited to);
  - Clustering Visualization and Analysis;
  - Time during the learning process;
  - Memory based systems;
  - User interaction models;
  - Fusion (Consensus) based models;
  - Clustering;
  - Feature selection;

SUBMISSION
The special session will be held as a part of the ICNNAI'2010 conference
(The 5th International Conference on Neural Network and Artificial
Intelligence ). The authors would submit papers through easychair site :
http://www.easychair.org/conferences/?conf=itlmdr10.

All paper submissions will be handled electronically. Detailed instructions
for submitting the papers are provided on the conference home page at
http://icnnai.bstu.by/icnnai-2010.html

Papers must correspond to the requirements detailed in the instructions to
authors from the ICNNAI 2010 web site. Accepted papers must be presented by
one of the authors to be published in the conference proceeding. If you have
any questions, do not hesitate to direct your questions to
nistor.grozavu at lipn.univ-paris13.fr

IMPORTANT DATES
   - Paper Submission Deadline: 4 April
   - Notification of acceptance: 22 April
   - Camera-ready papers: 29 April

ORGANIZERS
Nistor GROZAVU, Post-Doc, Computer Science Laboratory of Paris 13
University, FRANCE
Mustapha LEBBAH, Associate Professor at the Paris 13 University, FRANCE
Younes BENNANI, Full Professor at the Paris 13 University, FRANCE

Best regards,
Nistor Grozavu
PhD, Computer Science Laboratory of the Paris 13 University (LIPN)
http://www-lipn.univ-paris13.fr/~grozavu/
tel: +33 (0)626901790
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