Connectionists: CFP: Information Fusion Journal - Special Issue on Applications of Ensemble Methods (second notice)

[Nikunj Oza]

APOLOGIES FOR MULTIPLE COPIES
(This is a second announcement sent as a reminder. First announcement 
sent August 15, 2005)


                        Call for papers for a special issue of

                                    Information Fusion

An International Journal on Multi-Sensor, Multi-Source Information Fusion

                                 An Elsevier Publication

                                                On

                APPLICATIONS OF ENSEMBLE METHODS

Editor-in-Chief: Dr. Belur V. Dasarathy, FIEEE
d.belur at elsevier.com         http://belur.no-ip.com

Guest Editors: Nikunj C. Oza, Kagan Tumer

The Information Fusion Journal is planning a special issue devoted to
Applications of Ensemble Methods in Machine Learning and Pattern
Recognition. Ensembles, also known as Multiple Classifier Systems (MCSs)
and Committee Classifiers, were originally motivated by the desire to
avoid relying on just one learned model when only a small amount of
training data is available. Because of this, most studies on ensembles
have evaluated their new algorithms on relatively small datasets; most
notably, datasets from the University of California, Irvine (UCI)
Machine Learning Repository. However, modern data mining problems raise
a variety of issues very different from the ones ensembles have
traditionally addressed. These new problems include too much data; data
that are distributed, are noisy, and represent changing environments;
and performance measures different from the standard accuracy
measurements; among others. The aim of this issue is to examine the
different applications that raise these modern data mining problems, and
how current and novel ensemble methods aid in solving these problems.

Manuscripts (which should be original and not previously published or
presented even in a more or less similar form under any other forum)
covering new applications as well as the theories and algorithms of
ensemble learning algorithms developed to address these applications are
invited. Contributions should be described in sufficient detail to be
reproducible on the basis of the material presented in the paper.

Topics appropriate for this special issue include, but are not limited to:

· Innovative applications of ensemble methods.
· Novel algorithms that address unique requirements (for example,
different performance
measures or running time constraints) of an application or a class of
applications.
· Novel theories developed under assumptions unique to an application or
a class of applications.
· Novel approaches to distributed model fusion.

Manuscripts should be submitted electronically online at
http://ees.elsevier.com/inffus (The corresponding author will have to
create a user profile if one has not been established before at Elsevier.)
Please also send without fail an electronic copy to
oza at email.arc.nasa.gov (PDF format preferred),

                            Guest Editors
            Nikunj C. Oza and Kagan Tumer

            NASA Ames Research Center
            Mail Stop 269-3
            Moffett Field, CA 94035-1000, USA

            Deadline for Submission: November 30, 2005

-- 
--------------------------------------
Nikunj C. Oza, Ph.D.				Tel: (650)604-2978
Research Scientist				Fax: (650)604-4036
NASA Ames Research Center		E-mail: oza at email.arc.nasa.gov
Mail Stop 269-3					Web: http://ic.arc.nasa.gov/people/oza
Moffett Field, CA 94035-1000
USA