NDLI: The Generalization Performance of Regularized Regression Algorithms Based on Markov Sampling

Content Provider	IEEE Xplore Digital Library
Author	Bin Zou Yuan Yan Tang Zongben Xu Luoqing Li Jie Xu Yang Lu
Copyright Year	2013
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.
Page Count	11
File Size	9671530
Starting Page	1497
Ending Page	1507
File Format	PDF
ISSN	21682267
Volume Number	44
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Training Mean square error methods Kernel Machine learning algorithms Noise Cybernetics uniformly ergodic Markov chain Generalization performance Markov sampling regularized regression algorithms
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The Generalization Ability of Online SVM Classification Based on Markov Sampling

Generalization Performance of Fisher Linear Discriminant Based on Markov Sampling

The Generalization Ability of SVM Classification Based on Markov Sampling

Learning from regularized regression algorithms with p-order Markov chain sampling

The learning performance of support vector machine classification based on Markov sampling

Random sampling of random processes: Mean-square comparison of various interpolators

Stochastic Approximation for Consensus: A New Approach via Ergodic Backward Products

Stability Analysis of Stochastic Hybrid Jump Linear Systems Using a Markov Kernel Approach

Least Square Regularized Regression in Sum Space

The Generalization Performance of Regularized Regression Algorithms Based on Markov Sampling

Similar Documents

The Generalization Ability of Online SVM Classification Based on Markov Sampling

Generalization Performance of Fisher Linear Discriminant Based on Markov Sampling

The Generalization Ability of SVM Classification Based on Markov Sampling

Learning from regularized regression algorithms with p-order Markov chain sampling

The learning performance of support vector machine classification based on Markov sampling

Random sampling of random processes: Mean-square comparison of various interpolators

Stochastic Approximation for Consensus: A New Approach via Ergodic Backward Products

Stability Analysis of Stochastic Hybrid Jump Linear Systems Using a Markov Kernel Approach

Least Square Regularized Regression in Sum Space

The Generalization Performance of Regularized Regression Algorithms Based on Markov Sampling