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The growth function, also called the shatter coefficient or the shattering number, measures the richness of a set family or class of function. It is especially used in the context of statistical learning theory , where it is used to study properties of statistical learning methods.
Their enterprise-side product, HackerRank for Work, is a subscription service that aims to help companies source, screen (CodePair), and hire engineers and other technical employees. [12] The product is intended to allow technical recruiters to use programming challenges to test candidates on their specific programming skills and better ...
The first has two parallel reflection lines, the second two 2-fold gyrations, and the last has one mirror and one 2-fold gyration. In one dimension, the infinite dihedral group is seen in the symmetry of an apeirogon alternating two edge lengths, containing reflection points at the center of each edge.
Competitive programming or sport programming is a mind sport involving participants trying to program according to provided specifications. The contests are usually held over the Internet or a local network. Competitive programming is recognized and supported by several multinational software and Internet companies, such as Google, [1] [2] and ...
The hypersphere in 2 dimensions is a circle, sometimes called a 1-sphere (S 1) because it is a one-dimensional manifold. In a Euclidean plane, it has the length 2 ...
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
The Cremona group in 2 dimensions [ edit ] In two dimensions, Max Noether and Guido Castelnuovo showed that the complex Cremona group is generated by the standard quadratic transformation, along with P G L ( 3 , k ) {\displaystyle \mathrm {PGL} (3,k)} , though there was some controversy about whether their proofs were correct, and Gizatullin ...
However, for deterministic decision trees, and for any k in the range 2 ≤ k ≤ n, the property of containing a k-clique was shown to have decision tree complexity exactly n(n − 1)/2 by Bollobás (1976). Deterministic decision trees also require exponential size to detect cliques, or large polynomial size to detect cliques of bounded size. [68]