Neural network learning system inferring an input-output relationship from a set of given input and output samples
US5479576A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Feb 23, 1995 |
| Grant date | Dec 26, 1995 |
| Priority date | — |
| Expiry date | Feb 23, 2015 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A neural network learning system in which an input-output relationship is inferred. The system includes a probability density part for determining a probability density on a sum space of an input space and an output space from a set of given input and output samples by learning, the probability density on the sum space being defined to have a parameter, and an inference part for inferring a probability density function based on the probability density from the probability density part, so that an input-output relationship of the samples is inferred from the probability density function having a parameter value determined by learning, the learning of the parameter being repeated until the value of a predefined parameter differential function using a prescribed maximum likelihood method is smaller than a prescribed reference value.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.