Paper Info
Title
The generalization performance of regularized regression algorithms based on Markov sampling.
Abstract
This paper considers the generalization ability of two regularized regression algorithms [least square regularized regression (LSRR) and support vector machine regression (SVMR)] based on non-independent and identically distributed (non-i.i.d.) samples. Different from the previously known works for non-i.i.d. samples, in this paper, we research the generalization bounds of two regularized regression algorithms based on uniformly ergodic Markov chain (u.e.M.c.) samples. Inspired by the idea from Markov chain Monto Carlo (MCMC) methods, we also introduce a new Markov sampling algorithm for regression to generate u.e.M.c. samples from a given dataset, and then, we present the numerical studies on the learning performance of LSRR and SVMR based on Markov sampling, respectively. The experimental results show that LSRR and SVMR based on Markov sampling can present obviously smaller mean square errors and smaller variances compared to random sampling.
Year
DOI
Venue
2014
10.1109/TCYB.2013.2287191
IEEE T. Cybernetics
Keywords
DocType
Volume
markov sampling,generalization performance,generalization bounds,learning performance,regularized regression algorithms,uniformly ergodic markov chain,learning (artificial intelligence),regression analysis,numerical analysis,generalization ability,regularized regression algorithm,markov processes,markov sampling algorithm,least mean squares methods,mean square errors,svmr,nonindependent identically distributed samples,least square regularized regression,generalisation (artificial intelligence),sampling methods,support vector machine regression,lsrr,support vector machines
Journal
44
Issue
ISSN
Citations 
9
2168-2275
7
PageRank 
References 
Authors
0.47
13
6
Name
Order
Citations
PageRank
Bin Zou113013.96
Yuan Yan Tang22662209.20
Zongben Xu33203198.88
Luoqing Li447340.70
Jie Xu5438.19
Yang Lu6636.04