A Sequential Algorithm for Training Text Classifiers.¶
Lewis, D. D., & Gale, W. A. (1994). A Sequential Algorithm for Training Text Classifiers. Proceedings of SIGIR '94.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Theoretical Sampling
- The informativeness criterion is the crux and is exactly the frame's "steer by the model's weak points": rather than labeling a representative random sample, the algorithm ranks unlabeled examples by predictive uncertainty — the points nearest the decision boundary, where the model is least confident — because those are the cases that would most move the model, not merely thicken it.
This sourceIntroduces uncertainty sampling — querying the unlabeled examples nearest the decision boundary, where the model is least confident.
- The informativeness criterion is the crux and is exactly the frame's "steer by the model's weak points": rather than labeling a representative random sample, the algorithm ranks unlabeled examples by predictive uncertainty — the points nearest the decision boundary, where the model is least confident — because those are the cases that would most move the model, not merely thicken it.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:a5ce561e42fd · see in the full table