Method and system for training and neural network models for large number of discrete features for information rertieval
US11288573B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | May 5, 2016 |
| Grant date | Mar 29, 2022 |
| Priority date | — |
| Expiry date | Mar 17, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
According to one embodiment, a first set of features is received, where each of the features in the first set being associated with a predetermined category. A bloom filter is applied to the first set of features to generate a second set of features. A neural network model is trained by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection. The output of the neural network model is compared with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.