Non-linear classification of text samples
US9342794B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2013 |
| Grant date | May 17, 2016 |
| Priority date | — |
| Expiry date | Apr 11, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Non-linear classifiers and dimension reduction techniques may be applied to text classification. Non-linear classifiers such as random forest, Nyström/Fisher, and others, may be used to determine criteria usable to classify text into one of a plurality of categories. Dimension reduction techniques may also be used to reduce feature space size. Machine learning techniques may be used to develop criteria (e.g., trained models) that can be used to automatically classify text. Automatic classification rates may be improved and result in fewer numbers of text samples being unclassifiable or being incorrectly classified. User-generated content may be classified, in some embodiments.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.