This paper studies the classification properties and classification mechanisms of outer-supervised feed-forward neural network classifiers (FNNC). It is shown that nonlinear FNNCs can break through the 'bottleneck' behaviors for linear FNNCs. Assume that the involved FNNCs are classifiers that associate only one output node with each class, after the global minimum solutions with null costs based on batch-style learning are obtained, it is shown that in the case of the linear output network classifiers, the class weight vectors corresponding to different output nodes are orthogonal, and in the case of sigmoid output activation functions, the jth class weight vector must be situated in the negative direction of the i(i does not equal j) th class weight vector.

Date Published: 12 March 1999
PDF: 5 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341368

Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)