Graph-based transfer learning
US9477929B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 14, 2012 |
| Grant date | Oct 25, 2016 |
| Priority date | — |
| Expiry date | May 23, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. A graph-based transfer learning framework propagates label information from a source domain to a target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bipartite graph. An iterative algorithm renders the framework scalable to large-scale applications. The framework propagates the label information to both features irrelevant to the source domain and unlabeled examples in the target domain via common features in a principled way.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.