Self-organizing neural network for classifying pattern signatures with `a posteriori` conditional class probability
US5384895A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Aug 28, 1992 |
| Grant date | Jan 24, 1995 |
| Priority date | — |
| Expiry date | Aug 28, 2012 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0895
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A self-organizing neural network and method for classifying a pattern signature having N-features is provided. The network provides a posteriori conditional class probability that the pattern signature belongs to a selected class from a plurality of classes with which the neural network was trained. In its training mode, a plurality of training vectors is processed to generate an N-feature, N-dimensional space defined by a set of non-overlapping trained clusters. Each training vector has N-feature coordinates and a class coordinate. Each trained cluster has a center and a radius defined by a vigilance parameter. The center of each trained cluster is a reference vector that represents a recursive mean of the N-feature coordinates from training vectors bounded by a corresponding trained cluster. Each reference vector defines a fractional probability associated with the selected class based upon a ratio of i) a count of training vectors from the selected class that are bounded by the corresponding trained cluster to ii) a total count of training vectors bounded by the corresponding trained cluster. In the exercise mode, an input vector defines the pattern signature to be classified. T…
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