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.
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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.
Supported in partVerified against the work's full text
Lewis and Gale give uncertainty sampling: label the unlabeled examples the classifier is least sure of (probability nearest 0.5) instead of a random sample.
“Find the b examples for whic h the classi/er is least certain of class mem b ership”
- 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.
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