Generalization performance of least-square regularized regression algorithm with Markov chain samples |
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Authors: | Bin Zou Luoqing Li Zongben Xu |
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Affiliation: | 1. Faculty of Mathematics and Computer Science, Hubei University, Wuhan, 430062, China;2. Institute for Information and System Science, Faculty of Science, Xi?an Jiaotong University, Xi?an, 710049, China |
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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. |
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