Paper Info
Title
On the Minimization of Variables to Represent Partially Defined Classification Functions
Abstract
A partially defined classification function is a mapping from the set of k distinct vectors of n bite to m elements, wherek <; <; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . Such a function can often be represented with fewer variables than n, by appropriately assigning valus to don't cares. The number of variables can be further reduced by a linear transformation of the input variables. This paper shows an efficient method to find a linear transformation that reduces the number of variables. The method is illustrated with examples.
Year
DOI
Venue
2020
10.1109/ISMVL49045.2020.00-19
2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL)
Keywords
DocType
ISSN
linear decomposition,logic design,partially defined function,support minimization,classification
Conference
0195-623X
ISBN
Citations 
PageRank 
978-1-7281-5407-7
1
0.37
References 
Authors
0
1
Name
Order
Citations
PageRank
Tsutomu Sasao11083141.62