Call for Papers

Machine Learning Algorithms

for Event Detection

-- A Special Issue of the Machine Learning Journal --

Submission deadline: December 19, 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:

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:

 

We encourage prospective authors to contact us (e-mail to d.margin@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:

 

Schedule: