Active learning aims to reduce the amount of labels re- quired for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the labeling process that depends – among other things – on the classification task, the distribution of the data and the employed classification scheme. In this paper, we analyze different sampling criteria including a novel density-based criteria and demonstrate the importance to combine explo- ration and exploitation sampling criteria. We also show that a time-varying combination of sampling criteria often improves performance. Finally, by formulating the criteria selection as a Markov decision process, we propose a novel feedback-driven framework based on reinforcement learn- ing. Our method does not require prior information on the dataset or the sampling criteria but rather is able to adapt the sampling strategy during the learning process by expe- rience. We evaluate our approach on three challenging ob- ject recognition datasets and show superior performance to previous active learning methods.
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