Self-organizing feature map with improved performance by non-monotonic variation of the learning rate
US6965885B2 · kind B2 · utility
4Cited by
9References
14Claims
0Family size
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Key dates
| Filing date | Jan 22, 2002 |
| Grant date | Nov 15, 2005 |
| Priority date | — |
| Expiry date | Feb 17, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0895
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The learning rate used for updating the weights of a self-ordering feature map is determined by a process that injects some type of perturbation into the value so that it is not simply monotonically decreased with each training epoch. For example, the learning rate may be generated according to a pseudorandom process. The result is faster convergence of the synaptic weights.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.