Graph based sampling
US7827123B1 · kind B1 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 16, 2007 |
| Grant date | Nov 2, 2010 |
| Priority date | — |
| Expiry date | May 29, 2029 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An iterative method of sampling real world event data to generate a subset of data that is used for training a classifier. Graph Based Sampling uses an iterative process of evaluating and adding randomly selected event data sets to a training data set. In Graph Based Sampling, at each iteration, a two event data sets are randomly selected from a stored plurality of event data sets. A proximity function is used to generate a correlation or similarity value between each of these randomly selected real world event data sets, and the current training data set. One of the randomly selected event data sets is then added to the training data set based on the proximity value. This process of selection and addition is repeated until the subset of training set is a pre-determined size.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.