|Workshop Description||(Last updated 05 Dec 2000)|
The rapidly increasing volume of data collected for decision support applications in commercial, industrial, medical, and defense domains has made it a challenge to scale up knowledge discovery in databases (KDD), the machine learning and knowledge acquisition component of these applications. Many techniques currently applied to KDD admit enhancement through the wrapper approach, which uses empirical performance of inductive learning algorithms as feedback to optimize parameters of the learning system.
Wrappers include algorithms for performance tuning, especially: optimization of learning system parameters (hyperparameters) such as learning rates and model priors; control of solution size; and change of problem representation (or inductive bias optimization). Strategies for changing the representation of a machine learning problem include decomposition of learning tasks into more tractable subproblems; feature construction, or synthesis of more salient or useful input variables; and feature subset selection, also known as variable elimination (a form of relevance determination).
This workshop will explore current issues concerning wrapper technologies for KDD applications, including:
This workshop is intended for researchers in the area of machine learning, including practitioners of knowledge discovery in databases (KDD) and statistical and computational learning theorists. Intelligent systems researchers with an interest in high-performance computation and large-scale, real-world applications of data mining (e.g., inference and decision support) will also find this workshop of interest.
|Workshop Agenda||(Last updated 05 Dec 2000)|
The workshop will consist of a short tutorial, a preliminary discussion period, morning and afternoon paper sessions (each 1.5 hours) followed by open discussion sessions, and two invited talks by KDD practitioners. To foster student participation, we will hold a discussion session on student research after the second paper session. If there is interest, this will be extended to a poster session.
An afternoon parallel track following the invited talk(s) will be between two components of the program committee - the organizing committee and review committee, with the latter group consisting of researchers who will meet with students cf. the IJCAI and AAAI Graduate Student Consortia.
To allow time for informal discussions, we plan to limit most talks to 15-30 minutes, and hold a brainstorming session on special issues of journals and follow-up workshops and symposia at other conferences. To encourage participation but focus discussions on key topics, we invite 2-page research synopses and position papers from attendants who do not submit full papers.
|Important Dates||(Last updated 22 Mar 2001)|
|Full Papers due:||Monday, 02 April 2001 (extended deadline)|
|Short Papers due:||Friday, 06 April 2001|
|acceptance notification:||Monday, 09 April 2001|
|camera-ready copy due:||Friday, 20 April 2001 (extended deadline)|
|workshop||Saturday, 04 August 2001|
|Call for Papers and Submission Procedure||(Last updated 09 Mar 2001)|
We encourage submissions containing original theoretical and applied concepts in KDD. Experimental results are also encouraged, especially on fielded applications, even if they are only preliminary. We therefore invite two categories of paper submissions:
We request that authors prepare papers in the standard IJCAI format.
The first page of submitted papers should include the title; a brief abstract; and author names, affiliations, postal addresses, electronic mail addresses, and telephone and fax numbers.
Papers should be submitted in PostScript, PDF, or Microsoft Word 97/2000 file format. Electronic submissions are preferred, but both types of papers may be submitted by one of the following three options:
If you do not receive e-mail confirmation of the submission, please submit it again and send e-mail to the workshop chair (firstname.lastname@example.org).
Dr. William H. Hsu (primary contact)
Department of Computing and Information Sciences
p: Kansas State University, 234 Nichols Hall, Manhattan, KS 66506-2302 * t: (785) 539-7180 * f: (785) 539-7180 * e: email@example.com * w: www.cis.ksu.edu/~bhsu
Dr. Hillol Kargupta
Department of Electrical Engineering and Computer Science
p: University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 * t: (410) 455-3972 * f: (410) 455-3969 * e: firstname.lastname@example.org * w: www.cs.umbc.edu/~hillol
Dr. Huan Liu
Department of Computer Science and Engineering
p: Arizona State University, Box 875406, Tempe, AZ 85287-5406 * t: (480) 727-7349 * f: (480) 965-2751 * e: email@example.com * w: www.public.asu.edu/~huanliu
Dr. Nick Street
Department of Management Sciences
p: The University of Iowa, S210 Pappajohn Business Building, Iowa City, IA 52242-1000 * t: (319) 335-1016 * f: (319) 335-0297 * e: firstname.lastname@example.org * w: dollar.biz.uiowa.edu/~street
|Page created: 05 Dec 2000
Last updated: 27 Oct 2002 William H. Hsu