Baras, John S. and Dey, Subhrakanti (1999) Adaptive classification based on compressed data using learning vector quantization. In: Proceedings of the 38th IEEE Conference on Decision and Control. IEEE, pp. 3677-3683. ISBN 0780352505

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Abstract

Classification problems using compressed data are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from aided target recognition (ATR), to medical diagnosis, to speech recognition, to fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for the combined compression and classification problem. We show convergence of the algorithm using techniques from stochastic approximation, namely, the ODE method.

Item Type:	Book Section
Additional Information:	J. S. Baras and S. Dey, "Adaptive classification based on compressed data using learning vector quantization," Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999, pp. 3677-3683 vol.4, doi: 10.1109/CDC.1999.827925.
Keywords:	Learning vector quantization; classification; stochastic approximation; compression; non-parametric ;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14436
Identification Number:	https://doi.org/10.1109/CDC.1999.827925
Depositing User:	Subhrakanti Dey
Date Deposited:	18 May 2021 17:08
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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