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Proceedings Paper

Automatic generation of GRBF networks using the integral wavelet transform
Author(s): Shayan Mukherjee; Shree K. Nayar
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Paper Abstract

Learning can often be viewed as the problem of mapping from an input space to an output space. Examples of these mappings are used to construct a continuous function that approximates given data and generalizes for intermediate instances. Generalized Radial Basis Function (GRBF) networks are used to formulate this approximating function. A novel method is introduced that uses the Integrated Wavelet Transform to construct an optimal GRBF network for a given mapping and error bound. Simple 1D examples are used to demonstrate how the optimal network is superior to one constructed using standard ad hoc optimization techniques. The paper concludes with an application of optimal GRBF networks to a multidimensional problem (15 - 20 dimensions), real-time object recognition and pose estimation. The results of this application are favorable and the optimal GRBF network outperforms a GRBF network constructed using a traditional method.

Paper Details

Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, (1 September 1995); doi: 10.1117/12.217624
Show Author Affiliations
Shayan Mukherjee, Columbia Univ. (United States)
Shree K. Nayar, Columbia Univ. (United States)

Published in SPIE Proceedings Vol. 2569:
Wavelet Applications in Signal and Image Processing III
Andrew F. Laine; Michael A. Unser, Editor(s)

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