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Proceedings Paper

Back-propagation networks for classification in remote sensing
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Paper Abstract

The new data-driven weights initialization method for the back-propagation learning algorithm is proposed based on the generation of only those hyperplanes which are cutting the input data feature space. It allows to speed up the training of the learning algorithm and to decrease the possibility of getting trapped in a local minimum. The conventional way of weights initialization and the new method of weights initialization are investigated for synthetic XOR data and real remote sensing data, SAR. The back-propagation with the new weights initialization method showed the ability to provide consistently better results than the conventional way of weights initialization for the data investigated.

Paper Details

Date Published: 17 November 1995
PDF: 10 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226851
Show Author Affiliations
Gintautas Palubinskas, Univ. Stuttgart (Germany)

Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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