ERM Scheme for Quantile Regression
Dao-Hong Xiang
Abstract and Applied Analysis · 2013
doi:10.1155/2013/148490 (opens in a new tab)
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Abstract
This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are derived by applying concentration techniques involving the ℓ2-empirical covering numbers.
Abstract licence: CC BY
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