Model selection in machine learning with applications to document clustering
US6584456B1 · kind B1 · utility
40Cited by
11References
18Claims
0Family size
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Key dates
| Filing date | Jun 19, 2000 |
| Grant date | Jun 24, 2003 |
| Priority date | — |
| Expiry date | Mar 14, 2021 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A objective function based on a Bayesian statistical estimation framework is used to determine an optimal model selection by choosing both the optimal number of clusters and the optimal feature set. Heuristics can be applied to find the optimal (or at least sub-optimal) of this objective function in terms of the feature sets and the number of clusters, wherein the maximization of the objective function corresponds to the optimal model structure.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.