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Kohonen's Self-Organizing Feature Maps and Linear Vector Quantization: A Comparison

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 6
Year of Publication: 2015
Authors:
Kiran Bhowmick
Mansi Shah
10.5120/21707-4823

Kiran Bhowmick and Mansi Shah. Article: Kohonen's Self-Organizing Feature Maps and Linear Vector Quantization: A Comparison. International Journal of Computer Applications 122(6):33-35, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Kiran Bhowmick and Mansi Shah},
	title = {Article: Kohonen's Self-Organizing Feature Maps and Linear Vector Quantization: A Comparison},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {6},
	pages = {33-35},
	month = {July},
	note = {Full text available}
}

Abstract

Machine learning has evolved over the past years to become one of the major research fields in Computer Science. In simple words, Machine Learning can be described as the process of training a machine to learn from its outputs and improvise itself in order to optimize its outputs. One of the major branch of machine learning is Unsupervised Learning where in the machine is not given any kind of feedback but is expected to learn on its own ("without Supervision"). This paper aims at describing in detail and thus comparing two such neural networks: Kohonen's Self Organizing Feature Maps (KSOFM) and Linear Vector Quantization (LVQ).

References

  • Principles of Soft Computing, Wiley, S. N. Sivanandan & S. N. Deepa.