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Improving the sample complexity using global data

Date

Authors

Mendelson, Shahar

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

We study the sample complexity of proper and improper learning problems with respect to different q-loss functions. We improve the known estimates for classes which have relatively small covering numbers in empirical L2 spaces (e.g., log-covering numbers

Keywords

Keywords: Approximation theory; Convergence of numerical methods; Estimation; Polynomials; Probability; Regression analysis; Set theory; Learning problems; Information theory Fat-shattering dimension; Glivenko-Cantelli classes; Kernel machines; Learning sample complexity; Uniform convexity

URI

http://hdl.handle.net/1885/27137

Collections

ANU Research Publications

Source

IEEE Transactions on Information Theory

Type

Journal article

DOI

10.1109/TIT.2002.1013137

Restricted until

2037-12-31

Downloads

File

Description

01_Mendelson_Improving_the_sample_2002.pdf (616.31 KB)

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