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Proceedings Paper

Probabilistic spectral feature extraction technique for neural networks
Author(s): Young Ro Yoon; Okan K. Ersoy
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Paper Abstract

Artificial neural net models have been studied for many years in the hope of achieving human- like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controlling the size of the network, the input information must be preprocessed before being fed into the neural network. In this paper, a probabilistic spectral feature extraction technique (PSFET) with multiview spectral representations and its applications are described. During training and testing, the PSFET allows efficient extraction of useful information in addition to generating an input vector size for best classification performance by the following neural network. Experimental results indicate that the performance of the neural network increases in classification accuracy when PSFET is used at the input. The network also generalizes better.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162026
Show Author Affiliations
Young Ro Yoon, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Okan K. Ersoy, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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