Smooth ε-insensitive regression by loss symmetrization
Annual Conference Computational Learning Theory2003-12-01 21 citations21 references
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TL;DRA framework for solving regression problems by reduction to classification is described, based on symmetrization of margin-based loss functions commonly used in boosting algorithms, namely, the logistic loss and the exponential loss, which yields a smooth version of the e-insensitive hinge loss used in support vector regression. (Semantic Scholar)