Feature engineering in neural networks optimization
US11847551B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 16, 2022 |
| Grant date | Dec 19, 2023 |
| Priority date | — |
| Expiry date | Sep 16, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.