Improving the sample complexity using global data
Date
2002
Authors
Mendelson, Shahar
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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
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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
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Source
IEEE Transactions on Information Theory
Type
Journal article
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2037-12-31
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