Improving the sample complexity using global data

Date

2002

Authors

Mendelson, Shahar

Journal Title

Journal ISSN

Volume Title

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

Description

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

Citation

Source

IEEE Transactions on Information Theory

Type

Journal article

Book Title

Entity type

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License Rights

Restricted until

2037-12-31