Dynamic techniques for evaluating quality of clustering or classification system aimed to minimize the number of manual reviews based on Bayesian inference and Markov Chain Monte Carlo (MCMC) techniques
US8635172B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 7, 2011 |
| Grant date | Jan 21, 2014 |
| Priority date | — |
| Expiry date | Jul 17, 2032 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/217
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Performance of the machine learning technique is assessed using Bayesian analysis where previously grouped documents belonging to a machine-assigned class or cluster are presented to a human rater and the rater's assessment is fed to the Bayesian analysis processor that updates a Beta bionomial model with each document. The model represents the precision probability associated with the class or cluster under test. Monitoring the precision probability, the technique enforces a set of stopping rules corresponding to an acceptance/rejection assessment of the machine learning apparatus. A Markov Chain Monte Carlo process operates on the model to infuse the processing of each subsequent class or cluster with knowledge from those previously processed.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.