NDLI: On using logic synthesis for supervised classification learning

Content Provider	IEEE Xplore Digital Library
Author	Goldman, J.A. Axtell, M.L.
Copyright Year	1995
Description	Author affiliation: Wright Lab., Wright-Patterson AFB, OH, USA (Goldman, J.A.)
Abstract	Learning from data is the central theme of knowledge discovery in databases (KDD) and the machine learning (ML) community. In order to handle large databases, certain assumptions are necessary to make the problem tractable. Without introducing explicit domain knowledge, a natural assumption is Occam's Razor. However, the requirement to find solutions of low complexity is not limited to KDD and ML. For example, in the logic synthesis community, low-complexity solutions are sought for realizing circuits. Although the logic synthesis paradigms discussed are certainly not new, it is still a relatively unknown phenomenon when referring to these tools' ability as machine learning programs. We demonstrate the applicability of circuit design tools to the KDD and ML communities. Specifically, we exhibit results from C4.5 (a typical machine learning algorithm), Espresso (a 2-level minimization circuit design tool),and Function Extrapolation by Recomposing Decompositions (FERD).
Starting Page	198
Ending Page	205
File Size	783354
Page Count	8
File Format	PDF
ISBN	0818673125
ISSN	10823409
DOI	10.1109/TAI.1995.479515
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-05
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Circuit synthesis Machine learning Databases Logic circuits Machine learning algorithms Minimization methods Extrapolation Logic design Software tools Set theory
Content Type	Text
Resource Type	Article

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