Method for unsupervised neural network classification with back propagation
US5444796A · kind A · utility
Assignee
Inventor
Key dates
| Filing date | Oct 18, 1993 |
| Grant date | Aug 22, 1995 |
| Priority date | — |
| Expiry date | Oct 18, 2013 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S128/925
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.