@inproceedings{453bf71d4b4a4ed498ae1d251962e047,
title = "A Design Method for Multiclass Classifiers",
abstract = "Logic minimization is used to design multiclass classifiers for machine learning. This can be an alternative to a neural network. A partially defined classification function f is derived from the training set. Our multiclass classifier correctly classifies not only all the samples in the training set, but also much of samples in the unseen test set. To improve the test accuracy, 1) minimization of variables in f;2) minimization of the number of products in a ternary SOP for f; and 3) maximization of the number of literals in a ternary SOP for f, are performed. Experimental results using MNIST and fashion MNIST data set show that logic minimization improves the test accuracy. Our classifiers can be easily implemented by LUTs and glue logic.",
keywords = "MNIST, classification, digit recognition, generalization ability, machine learning, neural network, partially defined function, support minimization, ternary logic",
author = "Tsutomu Sasao and Yuto Horikawa and Yukihiro Iguchi",
note = "Funding Information: 1Supported in part by a Grant-in-Aid for Scientific Research of the JSPS. Publisher Copyright: {\textcopyright} 2021 IEEE.; 51st IEEE International Symposium on Multiple-Valued Logic, ISMVL 2021 ; Conference date: 25-05-2021 Through 27-05-2021",
year = "2021",
month = may,
doi = "10.1109/ISMVL51352.2021.00033",
language = "English",
series = "Proceedings of The International Symposium on Multiple-Valued Logic",
publisher = "IEEE Computer Society",
pages = "148--153",
booktitle = "Proceedings - 2021 IEEE 51st International Symposium on Multiple-Valued Logic, ISMVL 2021",
}