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Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 34, No. 1. (January 2012), pp. 187-193, doi:10.1109/tpami.2011.177 Key: citeulike:12049787
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We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to lead to good generalization ability. We show that these two properties are fundamentally at odds with each other: A sparse algorithm cannot be stable and vice versa. Thus, one has to trade off sparsity and stability in designing a learning algorithm. In particular, our general result implies that ℓ
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