A New Approach to Machine Learning Based on Functional Decomposition of Multi -Valued Functions
Published In
2021 IEEE 51st International Symposium on Multiple-Valued Logic (ISMVL)
Document Type
Citation
Publication Date
5-25-2021
Abstract
This paper presents a novel design of a hardware classifier based on combining modified Ashenhurst-Curtis Decomposition and modified multiplexer-based synthesis. The PSUD classifier brings three new contributions: an approach to solve the column multiplicity problem for both types of decomposition, the way how the multiple-valued variables are encoded and used, and a modification of the Ashenhurst-Curtis Decomposition. One of the biggest challenges in Boolean function decomposition is the variable partitioning problem. Thus, we introduce a new representation of two combined classifiers for multiple-valued functions to overcome the variable partitioning problem which allows finding the minimal column multiplicity and consequently to find high quality decompositions leading to good learning. The classifier gives very good accuracy results when tested on multi-valued Machine Learning benchmarks from the UC Irvine repository.
Rights
©2021 IEEE
Locate the Document
DOI
10.1109/ISMVL51352.2021.00030
Persistent Identifier
https://archives.pdx.edu/ds/psu/36028
Publisher
IEEE
Citation Details
Al-Askaar, S., & Perkowski, M. (2021). A New Approach to Machine Learning Based on Functional Decomposition of Multi -Valued Functions. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ismvl51352.2021.00030