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Efficient Training of Self Organizing Map Network for Pattern Recognition

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IJCA Proceedings on National Conference on Innovations and Recent Trends in Engineering and Technology
© 2014 by IJCA Journal
NCIRET - Number 3
Year of Publication: 2014
Authors:
Preksha Pareek
Bhaskar Bissa

Preksha Pareek and Bhaskar Bissa. Article: Efficient Training of Self Organizing Map Network for Pattern Recognition. IJCA Proceedings on National Conference on Innovations and Recent Trends in Engineering and Technology NCIRET(3):25-27, November 2014. Full text available. BibTeX

@article{key:article,
	author = {Preksha Pareek and Bhaskar Bissa},
	title = {Article: Efficient Training of Self Organizing Map Network for Pattern Recognition},
	journal = {IJCA Proceedings on National Conference on Innovations and Recent Trends in Engineering and Technology},
	year = {2014},
	volume = {NCIRET},
	number = {3},
	pages = {25-27},
	month = {November},
	note = {Full text available}
}

Abstract

Pattern recognition is the science which helps in getting inferences from input data, usage of tools from machine learning and other algorithm designing. Neural networks techniques are popular in the field of pattern recognition. The importance of Neural Network is that it provides very powerful framework for representing mappings from several input variables to output variables. Self Organizing Map(SOM) technique has been applied in this work where implementation of one-D, two-D SOM has been done and modified algorithm of SOM has been proposed. In SOM unsupervised learning is employed where targets are not specified. Implementation of this has been done in C++. As a result of this modified algorithm of SOM performs better than using architecture of one-D map and two-D map networks for some sets of patterns.

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