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Two frameworks for integrating knowledge in inductionThe use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.
Document ID
Document Type
Conference Paper
Rosenbloom, Paul S.
(University of Southern California Marina del Rey, CA, United States)
Hirsh, Haym
(University of Southern California Marina del Rey, CA, United States)
Cohen, William W.
(University of Southern California Marina del Rey, CA, United States)
Smith, Benjamin D.
(University of Southern California Marina del Rey, CA, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1994
Publication Information
Publication: NASA. Johnson Space Center, The Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), Volume 1
Subject Category
Documentation And Information Science
Accession Number
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.
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