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In this paper, we propose to reformulate the active learning problem occurring in classification as a sequential decision making problem. We particularly focus on the problem of dynamically allocating a fixed budget of samples. This raises the problem of the trade off between exploration and exploitation which is traditionally addressed in the framework of the multiarmed bandits theory. Based on previous work on bandit theory applied to active learning for regression, we introduce four noveldoi:10.1109/adprl.2014.7010610 dblp:conf/adprl/ColletP14 fatcat:ce7qevp5yvhe7mhejstbvx52wa