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On the convex hull of uniform random points in a simpled-polytope

TLDR
The sharp constants of the asymptotic expansion ofEn(f), En(v), andEn(V), asn tends to infinity are determined.
Abstract
Denote the expected number of facets and vertices and the expected volume of the convex hullPn ofn random points, selected independently and uniformly from the interior of a simpled-polytope byEn(f), En(v), andEn(V), respectively. In this note we determine the sharp constants of the asymptotic expansion ofEn(f), En(v), andEn(V), asn tends to infinity. Further, some results concerning the expected shape ofPn are given.

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Proceedings Article

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

TL;DR: In this paper, the authors take a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted $k$-nearest neighbor schemes.
Journal ArticleDOI

Approximation of convex sets by polytopes

TL;DR: A survey of polyhedral approximation of convex bodies can be found in this article, which contains results related to different aspects of different polyhedral approximations of the convex body and some adjacent problems.
Book

Kinetic data structures

TL;DR: A new type of data structure is created, called a kinetic data structure, made of a proof of correctness of the attribute which is animated through time by a discrete event simulation.

Random polytopes, convex bodies, and approximation

TL;DR: In this paper, a random polytope inscribed in a convex body was investigated, and it was shown that it is very close to the so-called floating body inscribed in K with parameter 1/n.
Journal ArticleDOI

Random points and lattice points in convex bodies

TL;DR: In this article, the authors consider the problem of finding a convex hull of a polytope whose vertices belong to a given subset of R-d and show that the maximal hull of such polytopes approximate K.
References
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Book

A Course of Modern Analysis

TL;DR: The volume now gives a somewhat exhaustive account of the various ramifications of the subject, which are set out in an attractive manner and should become indispensable, not only as a textbook for advanced students, but as a work of reference to those whose aim is to extend the knowledge of analysis.
Book

Integral geometry and geometric probability

Abstract: Part I. Integral Geometry in the Plane: 1. Convex sets in the plane 2. Sets of points and Poisson processes in the plane 3. Sets of lines in the plane 4. Pairs of points and pairs of lines 5. Sets of strips in the plane 6. The group of motions in the plane: kinematic density 7. Fundamental formulas of Poincare and Blaschke 8. Lattices of figures Part II. General Integral Geometry: 9. Differential forms and Lie groups 10. Density and measure in homogenous spaces 11. The affine groups 12. The group of motions in En Part III. Integral Geometry in En: 13. Convex sets in En 14. Linear subspaces, convex sets and compact manifolds 15. The kinematic density in En 16. Geometric and statistical applications: stereology Part IV. Integral Geometry in Spaces of Constant Curvature: 17. Noneuclidean integral geometry 18. Crofton's formulas and the kinematic fundamental formula in noneuclidean spaces 19. Integral geometry and foliated spaces: trends in integral geometry.