CFPFrom: Richard Dybowski firstname.lastname@example.org
Date: Tue, 12 Dec 2000 18:51:57 +0000
Subject: ML Journal Issue on Fusion of Knowledge with Data
Machine Learning Journal Special Issue on Fusion of Domain Knowledge with Data for Decision Support >>> First Call for Papers <<< Statistics and machine learning are data-oriented tasks in which domain models are induced from data. The bulk of research in these fields concentrates on inducing models from data archived in computer databases. However, for many problem domains, human expertise forms an essential part of the corpus of knowledge needed to construct models of the domain. The discipline of knowledge engineering has focused on encoding the knowledge of experts in a form that can be encoded into computational models of a domain. At present, knowledge engineering and machine learning remain largely separate disciplines. Yet in many fields of endeavor, substantial human expertise exists alongside data archives. When both data and domain knowledge are available, how can these two resources effectively be combined to construct decision support systems? The aim of this special issue of the Machine Learning journal is to allow researchers to communicate their work on integrating domain knowledge with data (knowledge-data fusion; theory revision; theory refinement) to a general machine learning audience. Emphasis is on sound theoretical frameworks rather than ad hoc approaches. Of particular interest are papers that combine clear theoretical discussion with practical examples, and papers that compare different approaches. Possible frameworks for knowledge-data fusion include probabilistic (Bayesian/belief) networks, possibilistic logics and networks, hybrid neuro-fuzzy networks, and inductive logic programming. Topics of interest include (but are not limited to): * Practical applications of knowledge-data fusion. What lessons have been learnt from attempts to apply knowledge-data fusion to real-world decision problems? * How are the various knowledge representation and inference frameworks that permit induction theoretically related to each other? * What frameworks enable an existing induced model, such as a neural network, to be incorporated into a proposed knowledge-based system? * How can knowledge-data fusion be applied to temporal data? Submitted papers must not exceed 30 pages and must conform to the Machine Learning journal style. Please see the associated Web site for further submission details: http://www.umds.ac.uk/microbio/richard/kdf/ This Call for Papers is *not* restricted to those who presented at the UAI 2000 Workshop on Knowledge-Data Fusion: it is open to everyone who has an interest in this topic. Please direct any enquiries to Richard Dybowski: email@example.com Schedule -------------- Paper submission deadline: June 1, 2001 Authors' notification of decisions: September 1, 2001 Final revised papers due: December 15, 2001 Guest Editors -------------------- Richard Dybowski (King's College London) Kathryn Blackmond Laskey (George Mason University) James Myers (Ballistic Missile Defense Organization) Simon Parsons (Liverpool University)
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