tailieunhanh - Báo cáo toán học: "Construction of Minimal Bracketing Covers for Rectangles"

Tuyển tập các báo cáo nghiên cứu khoa học về toán học trên tạp chí toán học quốc tế đề tài: Construction of Minimal Bracketing Covers for Rectangles. | Construction of Minimal Bracketing Covers for Rectangles Michael Gnewuch Department of Computer Science Kiel University Christian-Albrechts-Platz 4 24098 Kiel Germany email mig@ Submitted Sep 5 2007 Accepted Jul 16 2008 Published Jul 21 2008 Mathematics Subject Classifications 05B40 11K38 52C45 Abstract We construct explicit ỗ-bracketing covers with minimal cardinality for the set system of anchored rectangles in the two dimensional unit cube. More precisely the cardinality of these ỗ-bracketing covers are bounded from above by Ỗ 2 o ỗ 2 . A lower bound for the cardinality of arbitrary ỗ-bracketing covers for d-dimensional anchored boxes from M. Gnewuch Bracketing numbers for axis-parallel boxes and applications to geometric discrepancy J. Complexity 24 2008 154-172 implies the lower bound ỗ-2 O ỗ 1 in dimension d 2 showing that our constructed covers are essentially optimal. We study also other ỗ-bracketing covers for the set system of rectangles deduce the coefficient of the most significant term Ỗ 2 in the asymptotic expansion of their cardinality and compute their cardinality for explicit values of ỗ . 1 Introduction Entropy numbers are measures of the size of a given class F of functions or sets and they are frequently used in fields like density estimation empirical processes or machine learning. Good bounds for these entropy numbers in particular the covering or the bracketing numbers can . be used to prove bounds on the expectations of suprema of empirical processes as . Dudley s metric entropy bound concentration of measure results for these suprema or to verify that a class F of functions or sets is a Glivenko-Cantelli or Donsker Class . that the corresponding F-indexed empirical process Gn exhibits a certain convergence behavior as n tends to infinity cf. 4 20 23 . They are also useful in geometric discrepancy theory . in the theory of uniform distribution. Different facets of this theory are nicely described in the .

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