Bayesian active learning for maximal information gain on model parameters

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The fact that machine learning models, despite their advancements, are still trained on randomly gathered data is proof that a lasting solution to the problem of optimal data gathering has not yet been found. In this paper, we investigate whether a Bayesian approach to the classification problem can provide assumptions under which one is guaranteed to perform at least as good as random sampling. For a logistic regression model, we show that maximal expected information gain on model parameters is a promising criterion for selecting samples, assuming that our classification model is well-matched to the data. Our derived criterion is closely related to the maximum model change. We experiment with data sets which satisfy this assumption to varying degrees to see how sensitive our performance is to the violation of our assumption in practice.

TitelProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
Antal sider8
ISBN (Elektronisk)9781728188089
StatusUdgivet - 2020
Begivenhed25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italien
Varighed: 10 jan. 202115 jan. 2021


Konference25th International Conference on Pattern Recognition, ICPR 2020
ByVirtual, Milan

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