Connectionists: CFP: "MULTIPLE CLASSIFIERS AND HYBRID LEARNING PARADIGMS" (KES 2011 Invited Session)

[Edmondo Trentin]

	[Apologies for possible cross-postings]

  	   	    Call for papers

    "MULTIPLE CLASSIFIERS AND HYBRID LEARNING PARADIGMS"

     	       KES 2011 Invited session IS30

	       12, 13, and 14 September 2011
	          Kaiserslautern, Germany

       URL: http://www.dii.unisi.it/~trentin/IS30.html


IMPORTANT DATES:
----------------

30 March, 2011: Submission deadline (in LNCS format)
20 April, 2011: Notification of acceptance
1 May, 2011: Deadline for camera-ready papers (via PROSE)
1 May, 2011: Early Registration Deadline

Accepted papers by registered Authors will be included in the KES
Proceedings (published by Springer). Extended journal versions of
selected papers will be considered afterwards, too.


INTRODUCTION:
-------------

When facing difficult real-world applications, it is often unlikely
that an individual learning paradigm can actually yield the solution
sought (in spite of its theoretical generality) without a strong
co-operation with other, profoundly different modules building up
the overall system. For instance, artificial neural networks are known
to be mathematically "universal" machines, but satisfactory solutions
to complex tasks can hardly be achieved with a single feed-forward
connectionist architecture. Historically, this led to the development
of multiple neural network systems, namely mixtures of experts or
neural ensembles, taking benefit from the specialization of individual
nets over specific regions of the feature space, according to a
divide-and-conquer strategy. As an alternative, multiple classifier
systems were proposed, aiming at combining models that have different
nature (e.g., generalized linear discriminants, parametric probabilistic
models, neural nets) or aim (e.g., estimating a discriminant function,
or a class-posterior probability, or a likelihood). In other
circumstances, like in the case of hybrid hidden Markov
model/connectionist approaches, the combination between the underlying
paradigms relies on the idea of exploiting certain general properties
of one of them (e.g., the capability of modeling the long-term
dependencies in HMMs) with the strength of the other to accomplish
local, specific tasks that occur within the former (e.g., the capability
of flexible, discriminative modeling of the HMM emission probabilities
via neural nets). Along a similar direction, hybrid random fields were
introduced recently, They combine the overall, general structure of a
Markov random field with the optimal fit of conditional probabilities of
individual variables given their Markov blanket as obtained via Bayesian
networks. Again, maximum echo-state likelihood networks (MESLiN) were
proposed for sequence processing, relying on the combination of the
reservoir of an echo-state architecture with a parametric model of the
probability density function of the states of the reservoir. Strictly
related areas concern the integration between symbolic and sub-symbolic
learning machines, and the so-called information-fusion. In all these
scenarios, researchers are mostly concerned with the development and
investigation of plausible, mathematically sound techniques for
combining the different learners in a feasible, robust manner (instead
of just piling-up the different modules onto one another heuristically).
Such research efforts are leading to training algorithms that split
properly the original learning problem over the component machines,
training the latter ones according to a joint, global criterion which
fits the solution of the original, overall problem.


AIM OF THE SESSION:
--------------------

Aim of this Invited Session is to bring together researchers involved
in any area of pattern recognition and machine learning that is related
to these issues. Fellow scientists are invited to submit their paper(s)
to this Session, according to the guidelines for Authors and the
reviewing procedures which hold for the KES Conference hosting this
Session. Novel, fresh ideas are particularly welcome (even though in
preliminary form), although strong experimental analysis of established
approaches to severe real-world tasks is encouraged as well.


LIST OF TOPICS:
---------------

Topics of interest include (but they are not limited to):
 - multiple classifiers/regression models;
 - hybrid hidden Markov models/neural network systems;
 - hybrid random fields and other hybrid graphical models;
 - combination of kernel machines and other paradigms;
 - probabilistic interpretation of neural networks;
 - learners based on both symbolic and sub-symbolic paradigms;
 - information fusion;
 - alternate/mixed induction/deduction learners;
 - multitask learning;
 - semi-supervised learning;
 - hybrid approaches to relational learning and graph/structure
   processing;
 - deep architectures which hybridize supervised and unsupervised
   learning.


SUBMISSION:
-----------

Page formatting: for formatting information, please refer to the
Information for LNCS Authors at
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.

Please note that papers should be no longer than 10 pages in LNCS
format. Papers longer than this will be subject to an additional page
charge. All oral and poster papers must be presented by one of the
authors who must register within the KES Early Registration deadline.

Please submit your paper through the KES submission system (PROSE),
making sure you pick up the IS30 Invited Session item from the menu
(NOTE: this item is listed in the "invited Sessions" table, not in the
"General Sessions" list).

Every paper must have at least one author who has registered for the
conference with payment by the Early Registration Deadline for the
paper to appear in the proceedings.


REVIEW PROCESS:
---------------

All submissions will be reviewed on the basis of relevance, originality,
significance, soundness and clarity. At least two referees will review
each submission independently.


PUBLICATION:
------------

All accepted papers (of registered Authors) will be published in the
KES2011 Proceedings (LNCS/LNAI, Springer-Verlag).

Extended versions of selected papers will be considered for
publication in the KES Journal  (International Journal of
Knowledge-Based and Intelligent Engineering Systems) published by IOS
Press, and other selected journals.


JOINT EVENT:
------------

We are organizing PSL 2011 (Workshop on Partially Supervised Learning)
in Ulm, Germany, on September 8-9, 2011 (the official CFP will be
issued shortly), with submission deadline on May 6, 2011. If you are
planning to attend KES, please consider taking advantage of the
close-range between these events: your submissions to each of them
are welcome!


SESSION CHAIRS:
---------------

Edmondo Trentin
(http://www.dii.unisi.it/~trentin/HomePage.html)
Dipartimento di Ingegneria dell'Informazione
Universita' di Siena, I-53100 Siena (Italy)
E-mail: trentin AT dii DOT unisi DOT it

Friedhelm Schwenker
(http://www.informatik.uni-ulm.de/ni/staff/FSchwenker.html)
Department of Neural Information Processing
University of Ulm, D-89069 Ulm (Germany)
E-mail: friedhelm DOT schwenker AT uni-ulm DOT de


--------------------------------------------------------------------
Edmondo Trentin, PhD
Dip. Ingegneria dell'Informazione, V. Roma 56 - 53100 Siena (Italy)
E-mail:   trentin at dii.unisi.it
Voice:    +39-0577-234636
Fax:      +39-0577-233602
WWW:      http://www.dii.unisi.it/~trentin