Connectionists: MLJ Special Issue on Learning from Multiple Sources

[David R. Hardoon]

Apologies for multiple copies, please forward to whom ever may be  
interested.

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Machine Learning Journal
Special Issue on Learning from Multiple Sources

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http://www.cs.ucl.ac.uk/staff/D.Hardoon/LMS/

Important Dates:
Submission due - February 25, 2009
Notification - June 22, 2009
Camera ready - October 9, 2009

We would like to invite submissions for a special issue of the Machine  
Learning Journal on "Learning from Multiple Sources".

While the machine learning community has primarily focused on  
analysing the output of a single data source, there has been  
relatively few attempts to develop a general framework, or heuristics,  
for analysing several data sources in terms of a shared dependency  
structure. Learning from multiple data sources (or alternatively, the  
data fusion problem) is a timely research area. Due to the increasing  
availability and sophistication of data recording techniques and  
advances in data analysis algorithms, there exists many scenarios in  
which it is necessary to model multiple, related data sources. The  
open question is to find approaches to analyse data which consists of  
more than one set of observations (or view) of the same phenomenon.

The topics of interest include, but are not limited to:
	• Multi-view learning;
	• Multitask / Transfer learning;
	• Generative modelling of multiple related data sources;
	• Discriminative modelling of multiple related data sources;
	• Canonical correlation analysis-type methods;
	• Data fusion for real world applications;
		• Bioinformatics
		• Sensor networks
		• Multi-modal signal processing
		• Information retrieval
	• Online methods;
	• Generalisation error (learning theory) of multitask learning;
	• Machine learning research for related novel application domains.

Submissions are expected to represent high-quality, significant  
contributions in the area of machine learning algorithms and/or  
applications of machine learning. Application papers are expected to  
describe the application in detail and to present novel solutions that  
have some general applicability (beyond the specific application). The  
authors should follow standard formatting guidelines for Machine  
Learning Journal manuscripts.

Administrative notes:
	• Authors retain the copyrights to their papers. (See publication  
agreement on the MLJ website: http://pages.stern.nyu.edu/~fprovost/ 
MLJ/).
	• Submissions and reviewing will be handled electronically using  
standard procedures for Machine Learning (http://mach.edmgr.com).
	• Authors must register with the system before they can submit their  
manuscripts.
	• Accepted papers will be published electronically and citable  
immediately (before the print version appears).

Guest Editors:
Nicolò Cesa-Bianchi
Dipartimento di Scienze dell'Informazione
Università degli Studi di Milano
cesa-bianchi at dti.unimi.it

David R. Hardoon
University College London
Dept. of Computer Science
D.Hardoon at cs.ucl.ac.uk

Gayle Leen
Helsinki University of Technology
Adaptive Informatics Research Centre
Department of Information and Computer Science
gleen at cis.hut.fi



  
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