A Sequential Algorithm for Training Text Classifiers
About
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
David D. Lewis, William A. Gale• 1994
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Classification | CIFAR-100 (test) | Accuracy30 | 3518 | |
| Image Classification | CIFAR-10 (test) | Accuracy52.6 | 3381 | |
| Image Classification | FashionMNIST (test) | -- | 461 | |
| Image Classification | F-MNIST (test) | Accuracy58.8 | 173 | |
| Image Classification | ImageNet-50 (test) | -- | 48 | |
| Image Classification | ImageNet-10 (test) | Accuracy95.1 | 42 | |
| Domain Discovery | Retrieval Oracle Simulation | Domains Covered6 | 20 | |
| Image Classification | ImageNet 1k (test) | Final Accuracy66.5 | 12 | |
| Image Classification | ImageNet 10/50-class | Accuracy57.9 | 8 | |
| Active Learning for Cascaded Table Extraction | PubTables-1M | AUC71.99 | 6 |
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