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Statistical prediction with kanerva's sparse distributed memory
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Rogers, David |
| Copyright Year | 1989 |
| Description | A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory. |
| File Size | 498267 |
| Page Count | 12 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19920002525 |
| Archival Resource Key | ark:/13960/t4hn04k7r |
| Language | English |
| Publisher Date | 1989-01-01 |
| Access Restriction | Open |
| Subject Keyword | Statistics And Probability Distributed Processing Statistical Analysis Algorithms Mathematical Models Prediction Analysis Techniques Memory Computers Genetic Algorithms Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
