Classifying system having a single neural network architecture for multiple input representations
US5859925A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Aug 8, 1995 |
| Grant date | Jan 12, 1999 |
| Priority date | — |
| Expiry date | Aug 8, 2015 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A classification system is provided for combining multiple input representations by a single neural network architecture. In such a classification system having a single neural network architecture, classification channels corresponding to various input representations may be integrated through their own and shared hidden layers of the network to produce highly accurate classification. The classification system is particularly applicable to character classifying applications which use stroke and character image features as the main classification criteria, along with scalar features such as stroke count and aspect ratio features as secondary classification. The classification channels corresponding to the scalar features may be cross wired to the classification channels corresponding to the main input representations for further improving the accuracy of the classification output. Because a single neural network architecture is used, only one, standard training technique is needed for this classification system, special data handling is minimized, and the training time can be reduced, while highly accurate classification is achieved.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.