So entropy based active learning methods are an example of pool based sampling. Even within pool based sampling there a few different techniques.
Entropy selection for pool based methods looks at the output probability for each prediction of the model in the unlabelled data-set. Then it calculates the entropy of the distributions. (in classification this is a bit like looking for the most uniform predictive distributions) and prioritises those.
Entropy based active learning works ok but doesnt distinguish uncertainty that comes from a lack of knowledge (epistemic uncertainty) from noise. Techniques like Bayesian Active Learning by disagreement can do better. :)
Entropy selection for pool based methods looks at the output probability for each prediction of the model in the unlabelled data-set. Then it calculates the entropy of the distributions. (in classification this is a bit like looking for the most uniform predictive distributions) and prioritises those.
Entropy based active learning works ok but doesnt distinguish uncertainty that comes from a lack of knowledge (epistemic uncertainty) from noise. Techniques like Bayesian Active Learning by disagreement can do better. :)