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Generalization bounds of ERM algorithm with Markov chain samples

Content Provider	Springer Nature Link
Author	Xu, Jie Zou, Bin Xu, Zong ben
Copyright Year	2014
Abstract	One of the main goals of machine learning is to study the generalization performance of learning algorithms. The previous main results describing the generalization ability of learning algorithms are usually based on independent and identically distributed (i.i.d.) samples. However, independence is a very restrictive concept for both theory and real-world applications. In this paper we go far beyond this classical framework by establishing the bounds on the rate of relative uniform convergence for the Empirical Risk Minimization (ERM) algorithm with uniformly ergodic Markov chain samples. We not only obtain generalization bounds of ERM algorithm, but also show that the ERM algorithm with uniformly ergodic Markov chain samples is consistent. The established theory underlies application of ERM type of learning algorithms.
Ending Page	238
Page Count	16
Starting Page	223
File Format	PDF
ISSN	01689673
e-ISSN	16183932
Journal	Acta Mathematicae Applicatae Sinica
Issue Number	1
Volume Number	30
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2011-07-25
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	relative uniform convergence Theoretical, Mathematical and Computational Physics generalization bounds Applications of Mathematics Math Applications in Computer Science Analysis of algorithms uniformly ergodic Markov chain learning theory Pattern recognition, speech recognition ERM algorithm
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics

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