System and method for scalable cost-sensitive learning
US7904397B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 20, 2010 |
| Grant date | Mar 8, 2011 |
| Priority date | — |
| Expiry date | Jan 20, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method (and structure) for processing an inductive learning model for a dataset of examples, includes dividing the dataset of examples into a plurality of subsets of data and generating, using a processor on a computer, a learning model using examples of a first subset of data of the plurality of subsets of data. The learning model being generated for the first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of the dataset, the ensemble model thereby providing an evolving estimated learning model for the entirety of the dataset if all the subsets were to be processed. The generating of the learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of the ensemble model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.