Concentration estimates for learning with unbounded sampling (Q1946480)
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scientific article; zbMATH DE number 6153873
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Concentration estimates for learning with unbounded sampling |
scientific article; zbMATH DE number 6153873 |
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Concentration estimates for learning with unbounded sampling (English)
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15 April 2013
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Kernel based least square regression learning with unbounded sampling processes is studied. By a moment hypothesis for unbounded sampling, an approximation assumption for the regression function, and capacity condition for hypothesis space, sharper learning rates are derived. In the error analysis, a probability inequality for unbounded random variables is introduced, and an iteration technique is used.
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learning theory
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least-square regression
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regularization in reproducing kernel Hilbert spaces
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empirical covering number
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concentration estimates
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