Large scale manifold transduction that predicts class labels with a neural network and uses a mean of the class labels
US8266083B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 2, 2009 |
| Grant date | Sep 11, 2012 |
| Priority date | — |
| Expiry date | Feb 8, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24147
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for training a learning machine for use in discriminative classification and regression includes randomly selecting, in a first computer process, an unclassified datapoint associated with a phenomenon of interest; determining, in a second computer process, a set of datapoints associated with the phenomenon of interest that is likely to be in the same class as the selected unclassified datapoint; predicting, in a third computer process, a class label for the selected unclassified datapoint in a third computer process; predicting a class label for the set of datapoints in a fourth computer process; combining the predicted class labels in a fifth computer process, to predict a composite class label that describes the selected unclassified datapoint and the set of datapoints; and using the combined class label to adjust at least one parameter of the learning machine in a sixth computer process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.