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.
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Xu, J., Zou, B. (2009). Learning Performance of Tikhonov Regularization Algorithm with Strongly Mixing Samples. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_81
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DOI: https://doi.org/10.1007/978-3-642-01507-6_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
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