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Generalization performance of least-square regularized regression algorithm with Markov chain samples
- Source :
-
Journal of Mathematical Analysis & Applications . Apr2012, Vol. 388 Issue 1, p333-343. 11p. - Publication Year :
- 2012
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 0022247X
- Volume :
- 388
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Mathematical Analysis & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 70026349
- Full Text :
- https://doi.org/10.1016/j.jmaa.2011.11.032