Interactive feature selection for training a machine learning system and displaying discrepancies within the context of the document
US11023677B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 13, 2016 |
| Grant date | Jun 1, 2021 |
| Priority date | — |
| Expiry date | Jul 13, 2036 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L1/0072
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.