Supervision based grouping of patterns in hierarchical temporal memory (HTM)
US8195582B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2009 |
| Grant date | Jun 5, 2012 |
| Priority date | — |
| Expiry date | Dec 5, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A HTM network that uses supervision signals such as indexes for correct categories of the input patterns to group the co-occurrences detected in the node. In the training mode, the supervised learning node receives the supervision signals in addition to the indexes or distributions from children nodes. The supervision signal is then used to assign the co-occurrences into groups. The groups include unique groups and nonunique groups. The co-occurrences in the unique group appear only when the input data represent certain category but not others. The nonunique groups include patterns that are shared by one or more categories. In an inference mode, the supervised learning node generates distributions over the groups created in the training mode. A top node of the HTM network generates an output based on the distributions generated by the supervised learning node.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.