Optimal rates of aggregation

Alexandre B. Tsybakov

doi:10.1007/978-3-540-45167-9_23

Optimal rates of aggregation

Alexandre B. Tsybakov

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158 Citations (Scopus)

Résumé

We study the problem of aggregation of M arbitrary estimators of a regression function with respect to the mean squared risk. Three main types of aggregation are considered: model selection, convex and linear aggregation. We define the notion of optimal rate of aggregation in an abstract context and prove lower bounds valid for any method of aggregation. We then construct procedures that attain these bounds, thus establishing optimal rates of linear, convex and model selection type aggregation.

langue originale	Anglais
Pages (de - à)	303-313
Nombre de pages	11
journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2777
Les DOIs	https://doi.org/10.1007/978-3-540-45167-9_23
état	Publié - 1 janv. 2003
Evénement	16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003 - Washington, DC, États-Unis Durée: 24 août 2003 → 27 août 2003

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10.1007/978-3-540-45167-9_23

Contient cette citation

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abstract = "We study the problem of aggregation of M arbitrary estimators of a regression function with respect to the mean squared risk. Three main types of aggregation are considered: model selection, convex and linear aggregation. We define the notion of optimal rate of aggregation in an abstract context and prove lower bounds valid for any method of aggregation. We then construct procedures that attain these bounds, thus establishing optimal rates of linear, convex and model selection type aggregation.",

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003

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Optimal rates of aggregation

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