Aggregation via empirical risk minimization Export

@article{citeulike:3628889, abstract = {Abstract\ \ Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose risk is as close as possible to the risk of the best estimator in F. It was conjectured that empirical minimization performed in the convex hull of F is an optimal aggregation method, but we show that this conjecture is false. Despite that, we prove that empirical minimization in the convex hull of a well chosen, empirically determined subset of F is an optimal aggregation method.}, author = {Lecu\'{e}, Guillaume and Mendelson, Shahar}, citeulike-article-id = {3628889}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s00440-008-0180-8}, citeulike-linkout-1 = {http://www.springerlink.com/content/j51u38q873782870}, day = {1}, doi = {10.1007/s00440-008-0180-8}, journal = {Probability Theory and Related Fields}, keywords = {convexity, ensemble, erm, theory}, month = {November}, number = {3}, pages = {591--613}, posted-at = {2009-08-05 10:30:50}, priority = {1}, title = {Aggregation via empirical risk minimization}, url = {http://dx.doi.org/10.1007/s00440-008-0180-8}, volume = {145}, year = {2009} }

TY - JOUR ID - citeulike:3628889 L3 - citeulike-article-id:3628889 N2 - Abstract  Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose risk is as close as possible to the risk of the best estimator in F. It was conjectured that empirical minimization performed in the convex hull of F is an optimal aggregation method, but we show that this conjecture is false. Despite that, we prove that empirical minimization in the convex hull of a well chosen, empirically determined subset of F is an optimal aggregation method. IS - 3 JF - Probability Theory and Related Fields EP - 613 TI - Aggregation via empirical risk minimization VL - 145 SP - 591 KW - convexity KW - ensemble KW - erm KW - theory AU - Lecué, Guillaume AU - Mendelson, Shahar PY - 2009/11/01/ UR - http://dx.doi.org/10.1007/s00440-008-0180-8 ER -