Indextron

Alexei Mikhailov, Mikhail Karavay

2021

Abstract

How to do pattern recognition without artificial neural networks, Bayesian classifiers, vector support machines and other mechanisms that are widely used for machine learning? The problem with pattern recognition machines is time and energy demanding training because lots of coefficients need to be worked out. The paper introduces an indexing model that performs training by memorizing inverse patterns mostly avoiding any calculations. The computational experiments indicate the potential of the indexing model for artificial intelligence applications and, possibly, its relevance to neurobiological studies as well.

Paper Citation

in Harvard Style

Mikhailov A. and Karavay M. (2021). Indextron.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 143-149. DOI: 10.5220/0010180301430149

in Bibtex Style

@conference{icpram21,
author={Alexei Mikhailov and Mikhail Karavay},
title={Indextron},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={143-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010180301430149},
isbn={978-989-758-486-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Indextron
SN - 978-989-758-486-2
AU - Mikhailov A.
AU - Karavay M.
PY - 2021
SP - 143
EP - 149
DO - 10.5220/0010180301430149