Call for Papers DISCOVERY SCIENCE 2013
(http://www.mathematik.uni-marburg.de/~discoveryscience2013/)
The 16th International Conference on Discovery Science (DS-2013) will be held
in Singapore, on 6-9 October 2013, collocated with ALT-2013, the 24th
International Conference on Algorithmic Learning Theory. The two conferences
will be held in parallel, and will share their invited talks.
Traditionally the proceedings of DS series appear in the Lecture Notes in
Artificial Intelligence Series by Springer-Verlag. In addition, a special issue
on Discovery Science is planned in a prestigious journal.
DS 2013 provides an open forum for intensive discussions and exchange of new
ideas among researchers working in the area of Discovery Science. The scope of
the conference includes the development and analysis of methods for automatic
scientific knowledge discovery, machine learning, intelligent data analysis,
theory of learning, as well as their application to knowledge discovery.
Important dates are:
Full paper submission: May 11, 2013
Author notification: June 16, 2013
Camera-ready papers due: July 7, 2013
Early registration deadline: September 7, 2013
Conference: October 6-9, 2013
Submissions
We invite submissions of research papers addressing all aspects of discovery
science. We particularly welcome contributions that discuss the application of
data analysis, data mining and other support techniques for scientific
discovery including, but not limited to, biomedical, astronomical and other
physics domains.
Papers may contain up to fifteen (15) pages and must be formatted according to
the layout supplied by Springer-Verlag for the Lecture Notes in Computer
Science series.
Submitted papers may not have appeared in or be under consideration for
another conference or a journal, nor may they be under review or submitted to
another forum during the DS 2013 review process.
An excellent student paper will be selected to receive the Carl Smith Award The
award carries a scholarship prize of 555 euros.
Submission Topics
Possible topics include, but are not limited to:
* Logic and philosophy of scientific discovery
* Knowledge discovery, machine learning and statistical methods
* Ubiquitous Knowledge Discovery
* Data Streams, Evolving Data and Models
* Change Detection and Model Maintenance
* Active Knowledge Discovery
* Learning from Text and web mining
* Declarative approaches for data mining
* Information extraction from scientific literature
* Knowledge discovery from heterogeneous, unstructured and multimedia data
* Knowledge discovery in network and link data
* Knowledge discovery in social networks
* Data and knowledge visualization
* Spatial/Temporal Data
* Mining graphs and structured data
* Planning to Learn
* Knowledge Transfer
* Computational Creativity
* Human-machine interaction for knowledge discovery and management
* Biomedical knowledge discovery, analysis of micro-array and gene
deletion data
* Machine Learning for High-Performance Computing, Grid and Cloud Computing
* Applications of the above techniques to natural or social sciences
Best regards,
Eyke H�llermeier
Johannes F�rnkranz
Co-chairs of the PC
