@conference {
	title = {Adaptive Classification Based on Compressed Data Using Learning Vector Quantization},
	author = {Baras, John S., Dey, Subhrakanti.},
	year = {1999},
	month = {12/1999},
	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.},
	address = {Phoenix, Arizona},
	booktitle = {38th IEEE Conference on Decision and Control},
	pages = {pp. 3677-3683}
}
