Module for constructing trainable modular network in which each module inputs and outputs data structured as a graph
US6128606A · kind A · utility
53Cited by
4References
35Claims
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Key dates
| Filing date | Mar 11, 1997 |
| Grant date | Oct 3, 2000 |
| Priority date | — |
| Expiry date | Mar 11, 2017 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning paradigm called Graph Transformer Networks extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as inputs and produce graphs as output. Training is performed by computing gradients of a global objective function with respect to all the parameters in the system using a kind of back-propagation procedure. A complete check reading system based on these concept is described. The system uses convolutional neural network character recognizers, combined with global training techniques to provides record accuracy on business and personal checks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.