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- W2078432849 abstract "The previously known works describing the generalization of least-square regularized regression algorithm are usually based on the assumption of independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by studying the generalization of least-square regularized regression algorithm with Markov chain samples. We first establish a novel concentration inequality for uniformly ergodic Markov chains, then we establish the bounds on the generalization of least-square regularized regression algorithm with uniformly ergodic Markov chain samples, and show that least-square regularized regression algorithm with uniformly ergodic Markov chains is consistent." @default.
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- W2078432849 date "2012-04-01" @default.
- W2078432849 modified "2023-09-26" @default.
- W2078432849 title "Generalization performance of least-square regularized regression algorithm with Markov chain samples" @default.
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- W2078432849 doi "https://doi.org/10.1016/j.jmaa.2011.11.032" @default.
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