NDLI: An error reducing approach to machine learning using multi-valued functional decomposition

Content Provider	IEEE Xplore Digital Library
Author	Files, C.M. Perkowski, M.A.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., Portland State Univ., OR, USA (Files, C.M.)
Abstract	This paper considers minimization of incompletely specified multi-valued functions using functional decomposition. While functional decomposition was originally created for the minimization of logic circuits, this paper uses the decomposition process for both machine learning and logic synthesis of multi-valued functions. As it turns out, the minimization of logic circuits can be used in the concept of "learning" in machine learning, by reducing the complexity of a given data set. A main difference is that machine learning problems normally have a large number of output don't cares. Thus, the decomposition technique presented in this paper is focused on functions with a large number of don't cares. There have been several papers that have discussed the topic of using multi-valued functional decomposition for functions with a large number of don't cares. The novelty brought with this paper is that the proposed method is structured to reduce the resulting "error" of the functional decomposer where "error" is a measure of how well a machine learning algorithm approximates the actual, or true function.
Starting Page	167
Ending Page	172
File Size	150227
Page Count	6
File Format	PDF
ISBN	0818683716
ISSN	0195623X
DOI	10.1109/ISMVL.1998.679330
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Multivalued logic Network synthesis Circuit synthesis Minimization methods Field programmable gate arrays Logic circuits Joining processes Machine learning algorithms
Content Type	Text
Resource Type	Article
Subject	Mathematics Computer Science

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