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Proceedings Paper

Analyzing decision boundaries of neural networks
Author(s): Chulhee Lee; Eunsuk Jung
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Paper Abstract

In this paper, we analyze decision boundaries of 3 layer feedforward neural networks that use the sigmoid function as an activation function. By analyzing the decision boundaries in the space defined by the outputs of the hidden neurons, we found that the decision boundaries are always linear boundaries and that the decision boundaries are not completely independent. We found that for a 3-pattern class problem, the decision boundaries in the space defined by the outputs of the hidden neurons should meet at the same intersection. And this dependency of decision boundaries is extended to multiclass problems, providing valuable insight into decision boundaries. In particular, for a K-pattern classes problems, we found that there are only K-1 degree of freedoms in drawing decision boundaries in the space defined by the outputs of the hidden neurons, though there are KC2 decision boundaries. Finally, we present some interesting examples of decision boundaries of neural networks.

Paper Details

Date Published: 2 November 2000
PDF: 10 pages
Proc. SPIE 4113, Algorithms and Systems for Optical Information Processing IV, (2 November 2000); doi: 10.1117/12.405857
Show Author Affiliations
Chulhee Lee, Yonsei Univ. (South Korea)
Eunsuk Jung, Yonsei Univ. (South Korea)


Published in SPIE Proceedings Vol. 4113:
Algorithms and Systems for Optical Information Processing IV
Bahram Javidi; Demetri Psaltis, Editor(s)

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