Classification procedure implemented in a hierarchical neural ne

Details Classification procedure implemented in a hierarchical neural ne Classification procedure implemented in a hierarchical neural ne

1991-02-08

1993-06-08

Boudreau, Leo H.

Image analysis

Histogram processing

For setting a threshold

395 23, G06K 900

Patent

active

052186466

ABSTRACT:
Classification procedure implemented in a tree-like neural network which, in the course of learning steps, determines with the aid of a tree-like structure the number of neurons and their synaptic coefficients required for the processing of problems of classification of multi-class examples. Each neuron tends to distinguish, from the examples, two groups of examples approximating as well as possible to a division into two predetermined groups of classes. This division can be obtained through a principal component analysis of the distribution of examples. The neural network comprises a directory of addresses of successor neurons which is loaded in learning mode then read in exploitation mode. A memory stores example classes associated with the ends of the branches of the tree.

REFERENCES:
R. P. Lippmann, "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, Apr. 1987, pp. 4-22.
S. I. Gallant, "Optimal Linear Discriminants", IEEE proc. 8th Conf. on Pattern Recognition, Paris (1986), pp. 849-852.
M. Mezard & J. P. Nadal, "Learning in Feedforward Layered Networks: the tiling algorithm", J. Phys. A: Math. Gen. 22 (1989), pp. 2191-2203.
Diday et al., elements d'analyse de donnees, pp. 167-207 (no date).
Koutsougeras et al., "Training of a Neural Network for Pattern Classification Based on an Entropy Measure" IEEE Int. Conf. on Neural Networks, San Diego Calif. (1988) pp. I-247 through I-254.
Meir et al., "Iterated Learning in a Layer Feed-Forward Neural Network", Physical Review A, vol. 37, #7 pp. 2660-2668 (Apr. 1, 1988).

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