Scalable-effort classifiers for energy-efficient machine learning
US10783454B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 23, 2018 |
| Grant date | Sep 22, 2020 |
| Priority date | — |
| Expiry date | Jan 27, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Scalable-effort machine learning may automatically and dynamically adjust the amount of computational effort applied to input data based on the complexity of the input data. This is in contrast to fixed-effort machine learning, which uses a one-size-fits-all approach to applying a single classifier algorithm to both simple data and complex data. Scalable-effort machine learning involves, among other things, classifiers that may be arranged as a series of multiple classifier stages having increasing complexity (and accuracy). A first classifier stage may involve relatively simple machine learning models able to classify data that is relatively simple. Subsequent classifier stages have increasingly complex machine learning models and are able to classify more complex data. Scalable-effort machine learning includes algorithms that can differentiate among data based on complexity of the data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.