Volume 1, Issue 1, December 2016, Page: 15-18
Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images
P. Kiran Sree, Dept of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India
Sssn Usha Devi N., Dept of Computer Science and Engineering, University College of Engineering, Jawaharlal Nehru Technological University, Kakinada, India
Received: Nov. 27, 2016;       Accepted: Dec. 17, 2016;       Published: Jan. 16, 2017
DOI: 10.11648/j.mlr.20160101.12      View  2083      Downloads  90
Abstract
This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.
Keywords
Cellular Automata (CA), Active Learning (DL), Non Linear CA
To cite this article
P. Kiran Sree, Sssn Usha Devi N., Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images, Machine Learning Research. Vol. 1, No. 1, 2016, pp. 15-18. doi: 10.11648/j.mlr.20160101.12
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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