Classification with non-i.i.d. sampling

Authors:
Zheng-Chu Guo;Lei Shi
Affiliations:
School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou 510275, PR China and Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong, China;Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong, China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2011

Citing 9
Cited 0

The covering number in learning theory

Journal of Complexity
Support Vector Machine Soft Margin Classifiers: Error Analysis

The Journal of Machine Learning Research
Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)

Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)
Multi-kernel regularized classifiers

Journal of Complexity
Learning from dependent observations

Journal of Multivariate Analysis
Stability Bounds for Stationary φ-mixing and β-mixing Processes

The Journal of Machine Learning Research
Minimum complexity regression estimation with weakly dependent observations

IEEE Transactions on Information Theory - Part 2
Improving the sample complexity using global data

IEEE Transactions on Information Theory
Capacity of reproducing kernel spaces in learning theory

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.98

Visualization

Abstract

We study learning algorithms for classification generated by regularization schemes in reproducing kernel Hilbert spaces associated with a general convex loss function in a non-i.i.d. process. Error analysis is studied and our main purpose is to provide an elaborate capacity dependent error bounds by applying concentration techniques involving the @?^2-empirical covering numbers.