Call for Papers: JMLR Special Issue on Machine Learning Methods for Text
and  Images

Guest Editors:
   Jaz Kandola (Royal Holloway College, University of London, UK)
   Thomas Hofmann (Brown University, USA)
   Tomaso Poggio (M.I.T, USA)
   John Shawe-Taylor (Royal Holloway College, University of London, UK)

Submission Deadline: 29th March 2002

Papers are invited reporting original research on Machine Learning Methods
for Text and Images. This special issue follows the NIPS 2001 workshop on
the same topic, but is open also to contribution that were not presented
in it. A special volume will be published for this issue.

There has been much interest in information extraction from structured and
semi-structured data in the machine learning community.  This has in part
been driven by the large amount of unstructured and  semi-structured data
available in the form of text documents, images, audio, and video
files. In order to optimally utilize this data, one has to devise
efficient methods and tools that extract relevant information.

We invite original contributions that focus on exploring innovative and
potentially groundbreaking machine learning technologies as well as on
identifying key challenges in information access, such as multi-class
classification, partially labeled examples and the combination of evidence
from separate multimedia domains.

The special issue seeks contributions applied to text and/or images.

For a list of possible topics and information about the associated NIPS
workshop please see http://www.cs.rhul.ac.uk/colt/JMLR.html

Important Dates:

Submission Deadline: 29th March 2002
Decision:  24th June 2002
Final Papers: 24th July 2002

Many thanks

Jaz Kandola, Thomas Hofmann, Tommy Poggio and John Shawe-Taylor