No subject

financial assets remains a highly controversial question in finance (even if
recent publications in main scientific references seem to give some credits
to those works). From the methodological point of view, financial time
series appear to be very challenging. They are often characterized by a lot
of noise, problems of stationarity, sudden changes of volatility, ...

Neural networks have appeared as new tools in this area in this last decade.
This special session will try put to into light the serious results that we
can await form neural networks in this field and to analyze the
methodological issues of their application.


Artificial neural networks and early vision processing
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Organised by D. Charles, C. Fyfe, Univ. of Paisley (Scotland)

It is well known that biological visual systems, and in particular the human
visual system, are extraordinarily good at extracting deciphering very
complex visual scenes. Certainly, if consider the human visual system to be
solving inverse graphic problems then we have not really come close to
building artificial systems which are as effective as biological ones. We
have much to learn from studying biological visual architecture and the
implementation of practical vision based products could be improved by
gaining inspiration from these systems.

The following are some suggested areas of interest:

- Unsupervised preprocessing methods - e.g. development of local filters,
edge filtering.
- Statistical structure identification - e.g. Independent Component
Analysis, Factor Analysis, Principal Components Analysis, Projection
pursuit.
- Information theoretic techniques for the extraction/preservation of
information in visual data.
- Coding strategies - e.g. sparse coding, complexity reduction.
- Binocular disparity.
- Motion, invariances, colour encoding - e.g. optical flow, space/time
filters.
- Topography preservation.
- The practical application of techniques relating to these topics.


Artificial neural networks for Web computing
--------------------------------------------
Organised by M. Maggini, Univ. di Siena (Italy)

The Internet represents a new challenging field for the application of
machine learning techniques to devise systems which improve the
accessibility to the information available on the web. This domain is
particular appealing since it is easy to collect large amounts of data to be
used as training sets while it is usually difficult to write manually sets
of rules that solve interesting tasks. The aim of this special session is to
present the state of the art in the field of connectionist systems applied
to web computing. The possible fields for applications involve distributed
information retrieval issues like the design of thematic search engines,
user modeling algorithms for the personalization of services to access
information on the web, automatic security management, design and
improvement of web servers through prediction of request patterns, and so
on.

In particular the suggested topics are:
- Personalization of the access to information on the web
- Recommender systems on the web
- Crawling policies for search engines  Focussed crawlers
- Analysis and prediction of requests to web servers
- Intelligent chaching and proxies
- Security issues (e.g. intrusion detection)


Dedicated hardware implementations: perspectives on systems and applications
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Organised by D. Anguita, M. Valle, Univ. of Genoa (Italy)

The aim of this session is to assess new proposals for bridging the gap
between algorithms, applications and hardware implementations of neural
networks. Usually these three fields are not investigated in close
connection: researchers working in the development of dedicated hardware
implementations develop simplified versions of otherwise complex neural
algorithms or develop dedicated algorithms: usually these algorithms have
not been thoroughly tested on real-world applications. At the same time,
many theoretically sound algorithms are not feasible in dedicated hardware,
therefore limiting their success only to applications where a software
solution on a general-purpose system is feasible.

The focus of the session will be on the issues related to the hardware
implementation of neural algorithms and architectures and their successful
application to real world-problems, not on the details of the hardware
implementation itself.

The session will review both major achievements in hardware friendly
algorithms and assess major results obtained in the application of dedicated
neural hardware to real industrial and/or consumer applications.


Novel neural transfer functions
-------------------------------
Organised by W. Duch, Nicholas Copernicus Univ. (Poland)

It is commonly believed that because of universal approximation theorem
sigmoidal functions are sufficient for all applications. This belief has
been responsible for a slow progress in creating neural networks based on
novel transfer functions or using several transfer functions in one network.
Transfer functions are as important for creating good neural models as the
architectures and the training methods are because they have strong
influence on rates of convergence and on complexity of networks needed to
solve the problem at hand. This special session will be devoted to neural
models exploring the benefits of using different transfer functions. Papers
comparing results obtained with known and novel transfer functions,
developing methods of training suitable for heterogeneous function networks,
investigating theoretical rates of convergence or deriving approximations to
biological neural activity are strongly encouraged.


Neural networks and evolutionary/genetic algorithms - hybrid approaches
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Organised by T. Villmann, Univ. Leipzig (Germany)

Artificial neural networks can be taken as a special kind of learning and
self-adapting data processing systems. The abilities to handle noisy and
high-dimensional data, nonlinear problems, large data sets etc. using neural
techniques have lead to an innumerous number of applications as well as a
good theory behind.

An other adaptation approach is the approach of genetic and evolutionary
algorithms. One of the most advantages of these methods is the relative
independence of the algorithm according to the optimization goal defined by
the fitness function. The fitness function can comprise traditional
restrictions but may also include explicit expert knowledge.

In the last years several approaches were developed combining both neural
networks and genetic/evolutionary algorithms. Thereby, the methods ranging
from neural network learning using genetic algorithms and structure
adaptation of neural network topologies by genetic algorithms to migration
dynamic in evolutionary algorithms according to neural network dynamics and
other. Of coarse, combining both approaches should improve the capability of
the resulting hybrid system.

Authors of this special session are invited to submit actual contributions
which cover the above shortly but not completely explained area of hybrid
systems combining neural networks and genetic/evolutionary algorithms.
Thereby new methods and theoretical developments should be emphasized.
However, new applications with an interesting theoretical background are
also of interest.

Possible topics may be (but not restricted for further):
- neural network adaptation by genetic/evolutionary algorithms
- learning in neural networks using genetic/evolutionary algorithms
- clustering, fuzzy clustering by genetic/evolutionary algorithms
- neural networks for genetic/evolutionary algorithms
- applications using hybrid systems




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ESANN - European Symposium on Artificial Neural Networks
http://www.dice.ucl.ac.be/esann

* For submissions of papers, reviews,...
Michel Verleysen
Univ. Cath. de Louvain - Microelectronics Laboratory
3, pl. du Levant - B-1348 Louvain-la-Neuve - Belgium
tel: +32 10 47 25 51 - fax: + 32 10 47 25 98
mailto:esann at dice.ucl.ac.be

* Conference secretariat
D facto conference services
27 rue du Laekenveld - B-1080 Brussels - Belgium
tel: + 32 2 420 37 57 - fax: + 32 2 420 02 55
mailto:esann at dice.ucl.ac.be
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