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**Learning rates of regularized regression for exponentially strongly mixing sequence.**
*(English)*
Zbl 1134.62050

Summary: The study of regularized learning algorithms associated with least squared loss is one of the very important issues. Q. Wu et al. [Learning rates of least-square regularized regression. Found. Comput. Math. 6, No. 2, 171–192 (2006; Zbl 1100.68100)] established fast learning rates \(m^{-\theta }\) for the least squares regularized regression in reproducing kernel Hilbert spaces under some assumptions on Mercer kernels and on regression functions, where \(m\) denoted the number of the samples and \(\theta \) may be arbitrarily close to 1. They assumed, as in most existing works, that the set of samples were drawn independently from the underlying probability. However, independence is a very restrictive concept. Without the independence of samples, the study of learning algorithms is more involved, and little progress has been made. The aim of this paper is to establish the above results of Wu et al. for dependent samples. The dependence of samples in this paper is expressed in terms of exponentially strongly mixing sequences.

### MSC:

62J99 | Linear inference, regression |

68T05 | Learning and adaptive systems in artificial intelligence |

46N30 | Applications of functional analysis in probability theory and statistics |

65C60 | Computational problems in statistics (MSC2010) |

### Citations:

Zbl 1100.68100
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XMLCite

\textit{Y.-L. Xu} and \textit{D.-R. Chen}, J. Stat. Plann. Inference 138, No. 7, 2180--2189 (2008; Zbl 1134.62050)

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### References:

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