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The authors study online supervised learning under the empirical zero-one loss and introduce a novel classification algorithm with strong theoretical guarantees. The proposed method is a highly dynamical self-organizing decision tree structure, which adaptively partitions the feature space into small regions and combines (takes the union of) the local simple classification models specialized in those...
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