Image analysis – Learning systems – Neural networks
Patent
1993-10-18
1995-08-22
Boudreau, Leo H.
Image analysis
Learning systems
Neural networks
395 23, G06K 900
Patent
active
054447968
ABSTRACT:
An unsupervised back propagation method for training neural networks. For a set of inputs, target outputs are assigned 1's and 0's randomly or arbitrarily for a small number of outputs. The learning process is initiated and the convergence of outputs towards targets is monitored. At intervals, the learning is paused, and the values for those targets for the outputs which are converging at a less than average rate, are changed (e.g., 0.fwdarw.1, or 1.fwdarw.0), and the learning is then resumed with the new targets. The process is continuously iterated and the outputs converge on a stable classification, thereby providing unsupervised back propagation. In a further embodiment, samples classified with the trained network may serve as the training sets for additional subdivisions to grow additional layers of a hierarchical classification tree which converges to indivisible branch tips. After training is completed, such a tree may be used to classify new unlabelled samples with high efficiency. In yet another embodiment, the unsupervised back propagation method of the present invention may be adapted to classify fuzzy sets.
REFERENCES:
patent: 5182794 (1993-01-01), Gasperi et al.
patent: 5247206 (1993-09-01), Castro
patent: 5276770 (1994-01-01), Castelaz
patent: 5359699 (1994-10-01), Tong et al.
patent: 5384895 (1995-01-01), Rogers et al.
Bayer Corporation
Boudreau Leo H.
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