Learning Performance of Tikhonov Regularization Algorithm with Strongly Mixing Samples

Authors:
Jie Xu;Bin Zou
Affiliations:
Faculty of Mathematics and Computer Science, Hubei University, Wuhan, China 430062;Faculty of Mathematics and Computer Science, Hubei University, Wuhan, China 430062 and Institute for Information and System Science, Faculty of Science, Xi'an Jiaotong University, Xi'an, China 710 ...
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

The generalization performance is the main purpose of machine learning theoretical research. The previous bounds describing the generalization ability of Tikhonov regularization algorithm are almost all based on independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by establishing the bound on the generalization ability of Tikhonov regularization algorithm with exponentially strongly mixing observations. We then show that Tikhonov regularization algorithm with exponentially strongly mixing observations is consistent.