Call for Papers and Contributions
ICML 2006 Workshop on Machine Learning Algorithms for
Surveillance and Event Detection
Organizers
Denver Dash (Intel Research)
Terran Lane (University of New Mexico)
Dragos Margineantu (Boeing)
Weng-Keen Wong (Oregon State University)
Workshop Description
A common task in many domains involves monitoring routinely collected
data for anomalous events. Typically, this inspection of data is
performed for surveillance purposes. For instance, a security guard
needs to examine video footage for any signs of an intrusion.
Scientists also perform a similar task when analyzing experimental
data. Detection of anomalous events in experimental data often leads
to new scientific discoveries. In order to refer to this monitoring
process in as general a term as possible, we will call it event
detection. Event detection has the potential to impact a wide range
of important real-world applications, ranging from security, finance,
public health, medicine, biology, environmental science,
manufacturing, astrophysics, business and economics.
In the recent past, human beings have had the laborious job of
manually examining collected data for event detection; however, the
emergence of computers and massive world interconnectivity have made
it easier to collect data and have provided more reasons to do so.
Simple forms of data, such as a univariate time series, can be
effectively monitored using well-established techniques such as
regression, Box-Jenkins models and methods in statistical quality
control. Data, however, has become increasingly complex in recent
years. Multivariate records, images, video footage, audio
recordings, spatial and spatio-temporal data, text documents, and
even relational data are now routinely collected. One might expect
that existing work in machine learning would be well-suited for this
task. However, in practice, the peculiarities of the application
often grossly violate the standard assumptions of machine learning.
Often, none of the standard paradigms of supervised learning,
unsupervised learning or even semi-supervised and active learning fit
this situation well. As a result, new algorithms need to be created
in order to address these issues and fill an important gap in machine
learning research which would impact many of the most pressing real-
world applications being studied today.
The presence of event detection problems seems to be widespread
throughout areas of machine learning. This workshop will provide a
forum for participants from many different communities to share their
ideas and experiences. In addition, the workshop will benefit from
the diversity of ICML attendees, who span the entire spectrum from
applied to theoretical research. 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 neuroimaging data, and
relational data
* Event detection based on multiple data sources
* Integration of data mining components and expert 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
* Limiting and reducing false alarm rates, analysis of error
tradeoffs, cost models for event detection
* Validation and testing of event detection systems, metrics for
their performance
* Failures in applying data mining and learning on event detection
* Dealing with adversaries who attempt to remain undetected
* Novel application domains
This workshop is intended to be a continuation of the Workshop on
Data Mining Methods for Anomaly Detection at KDD 2005 but with a
greater emphasis on event detection in richer data representations
such as spatio-temporal data, relational data, and unstructured data
such as multimedia files.
Workshop Format
The workshop will consist of a combination of invited talks,
presentations of papers, and a discussion period. Each presentation
will be allocated 10-20 minutes for presentation and 10 minutes for
discussion. Attendance for the workshop will be limited to 40-50
people.
Participation and Submissions
To participate in the workshop, please send an email message to Weng-
Keen Wong ([EMAIL PROTECTED]) giving your name, affiliation,
address, email address, and a brief description of your reasons for
wanting to attend. The webpage for the workshop is at http://
web.engr.oregonstate.edu/~wong/workshops/icml2006/
Important Dates
April 28, 2006 (tentative) Workshop Submissions due
May 19, 2006 (tentative) Notification of acceptance
June 18, 2006 Workshop proceedings Posted on webpage
June 29, 2006 ICML Workshops
--
Terran Lane [EMAIL PROTECTED]
WWW=http://www.cs.unm.edu/~terran/
GPG key=http://www.cs.unm.edu/~terran/gpg_pub_key.shtml
"But I don't want to go among mad people," Alice remarked.
"Oh, you can't help that," said the Cat: "we're all mad here. I'm
mad. You're mad."
"How do you know I'm mad?" said Alice.
"You must be," said the Cat, "or you wouldn't have come here."
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