Connectionists: CFP: Machine Learning Algorithms for Event Detection

[Dash, Denver H]

                    --------------------------------
                            Call for Papers
                    --------------------------------

                      Machine Learning Algorithms

                                 for

                           Event Detection

              A Special Issue of the Machine Learning Journal

                 Submission deadline: November 28, 2007

 
http://www.pittsburgh.intel-research.net/~dhdash/mlj_eventdetection.html

      Denver Dash, Dragos Margineantu, and Weng-Keen Wong, guest editors

We would like to invite submissions for a special issue of the Machine 
Learning Journal on "Machine Learning Algorithms for Event Detection".

Event Detection is the task of monitoring a data source and detecting
the 
occurrence of an event that is captured within that source. There are
several 
sources of complexity for recent applications of event detection
problems: 
	* The variety of data sources is exploding, encompassing
multivariate
  	  records, images, video, audio, spatio-temporal data, text
documents, 
	  unstructured data and relational data;
	* The volume of data can be enormous, often measured in
Terabytes;
	* Applications often involve monitoring of human life or
critical 
	  assets and thus require extreme timeliness, high true-positive
rates 
	  or low false-positive rates;
	* The event may be localized or distributed in time and/or
space;
	* The event may be a never-before-seen "day-zero" event, which
does not
	  exist in training data;
	* The data source can be a single sensor, an array of identical
sensors
	  or an inhomogeneous mix of various sensors;
	* The problem is often exacerbated by the presence of an active 
	  adversary. 

These complexities pose an array of challenges for machine learning.
Often the 
standard paradigms of supervised learning, unsupervised learning or even
semi-
supervised and active learning do not fit the event detection problems
well.   
Addressing these issues would thus fill some important gaps in machine
learning
research and would impact many of the most pressing real-world
applications 
being studied today, such as security, public health, biology,
environmental 
sciences, manufacturing, astrophysics, finance, and business. 

The topics of interest include, but are not limited to:

	* Event detection in complex data such as video, audio,
spatio-temporal
	  data, text documents, functional neuro-imaging data, and
relational 
	  data;
	* Anomaly detection;
	* Monitoring and surveillance based on sensor data and on
multiple 
	  data sources;
	* The integration of learning and domain knowledge for event
detection;
	* Analysis of the capabilities of learning algorithms for event 
	  detection;
	* Automated event detection in safety-critical applications;
	* Algorithms and tools for online event detection;
	* Online limiting of false alarm rates, analysis of error
tradeoffs, 
	  risk models;
	* Scaling up detection algorithms to large populations;
	* Distributed algorithms for monitoring and surveillance;
	* Validation and testing of event detection and surveillance
systems, 
	  and metrics for their performance;
	* Dealing with adversaries in surveillance tasks;
	* Machine learning research for related novel application
domains.

We encourage prospective authors to contact us (e-mail to
d.margin at comcast.net)
with a brief summary of their paper concept for feedback, especially for
survey
papers or for papers focused on applications.

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.
	* Authors must select the appropriate Article Type, "Machine
Learning 
	  for Event Detection", when submitting their manuscripts.
	* Accepted papers will be published electronically and citable 
	  immediately (before the print version appears).

Schedule:
---------

	* Submission Deadline: November 28, 2007.
	* Acceptance Decisions: March 10, 2008.
	* Camera-Ready Papers Due: May 5, 2008.