Call for Papers
for Event Detection
-- A Special
Issue of the Machine Learning Journal --
deadline: December 19, 2007
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
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
The topics of
interest include, but are not limited to:
detection in complex data such as video, audio, spatio-temporal
data, text documents, functional neuro-imaging
data, and relational data;
and surveillance based on sensor data and on multiple data sources;
integration of learning and domain knowledge for event detection;
- Analysis of
the capabilities of learning algorithms for event detection;
event detection in safety-critical applications;
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;
algorithms for monitoring and surveillance;
and testing of event detection and surveillance systems, and metrics for
- Dealing with
adversaries in surveillance tasks;
learning research for related novel application domains.
We encourage prospective authors to contact us (e-mail to
email@example.com) 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.
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.
papers will be published electronically and citable immediately (before
the print version appears).
Deadline: December 19, 2007.
- Acceptance Decisions:
June 30, 2008.
- Camera-Ready Papers
Due: August 15, 2008.