Multi-valued functional decomposition as a machine learning method

• Published in: Proceedings / International Symposium on Multiple-Valued Logic pp. 173 - 178
• Main Authors: Files, C.M., Perkowski, M.A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 1998
• Subjects: Circuit synthesis; Encoding; Field programmable gate arrays; Learning systems; Machine learning; Minimization; Multivalued logic; Network address translation; Programmable logic arrays; Read only memory
• ISBN: 0818683716; 9780818683718
• ISSN: 0195-623X; 2378-2226
• DOI: 10.1109/ISMVL.1998.679331

In the past few years, several authors have presented methods of using functional decomposition as applied to machine learning. These authors explore the ideas of functional decomposition, but left the concepts of machine learning to the papers that they reference. In general, they never fully explain why a logic synthesis method should be applied to machine learning. This paper explores and presents the basic concepts of machine learning, and how some concepts match nicely with multi-valued logic synthesis, while others pose great difficulties. The main reason for using multi-valued synthesis is that many problems are naturally multi-valued (i.e., values taken from a discrete set). Thus, mapping the problem directly to a multi-valued set of inputs and outputs is much more natural than encoding the problem into a binary form. The paper also shows that any multi-valued logic synthesis method could be applied to the machine learning problem. But, this paper focuses on multivalued functional decomposition because of its generality of minimizing a given data set.