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We propose a new approach to large-scale machine learning, learning over compressed data: First compress the training data somehow and then em-ploy various machine learning algorithms on the compresse...d data, with the hope that the computation time is signi_cantly reduced when the training data is well compressed. As a _rst step toward this approach, we consider a variant of the Zero-Suppressed Binary Decision Diagram (ZDD) as the data structure for representing the training data, which is a generalization of the ZDD by incorporating non-determinism. For the learning algorithm to be employed, we consider a boosting algorithm called AdaBoost_ and its precursor AdaBoost. In this paper, we give efficient implementations of the boosting algorithms whose running times (per iteration) are linear in the size of the given ZDD.続きを見る
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