Methods and apparatus for generating a data classification model using an adaptive learning algorithm
US7987144B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 14, 2000 |
| Grant date | Jul 26, 2011 |
| Priority date | — |
| Expiry date | Feb 7, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a learning algorithm that adapts through experience. The present invention classifies objects in domain datasets using data classification models having a corresponding bias and evaluates the performance of the data classification. The performance values for each domain dataset and corresponding model bias are processed to identify or modify one or more rules of experience. The rules of experience are subsequently used to generate a model for data classification. Each rule of experience specifies one or more characteristics for a domain dataset and a corresponding bias that should be utilized for a data classification model if the rule is satisfied. The present invention dynamically modifies the assumptions (bias) of the learning algorithm to improve the assumptions embodied in the generated models and thereby improve the quality of the data classification and regression systems that employ such models. The disclosed self-adaptive learning process will become increasingly more accurate as the rules of experience are accumulated over time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.