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Proceedings Paper

Piecewise quadratic neural network for pattern classification (Proceedings Only)
Author(s): Sanjay S. Natarajan; David P. Casasent
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Paper Abstract

A neural network pattern classifier is presented. Its decision boundaries are formed from segments of conic sections which allows it to achieve improved performance over piecewise linear neural network classifiers, such as our earlier adaptive clustering neural network (ACNN). We discuss an optical realization that uses complex-valued weights, optical intensity detectors, and an additional input neuron to achieve piecewise conic decision surfaces (rather than the piecewise linear surfaces that the ACNN produces).

Paper Details

Date Published: 1 March 1992
PDF: 6 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135115
Show Author Affiliations
Sanjay S. Natarajan, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)

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