-
作者：Bérard, J; Bienvenüe, A
作者单位：Universite Claude Bernard Lyon 1; Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA)
摘要：We study the asymptotic behavior of a mutation-selection genetic algorithm on the integers with finite population of size p greater than or equal to 1. The mutation is defined by the steps of a simple random walk and the fitness function is linear. We prove that the normalized population satisfies an invariance principle, that a large-deviations principle holds and that the relative positions converge in law. After n steps, the population is asymptotically around rootn times the position at ti...
-
作者：Mossel, E; Peres, Y
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Consider a tree network T, where each edge acts as an independent copy of a given channel M, and information is propagated from the root. For which T and M does the configuration obtained at level n of T typically contain significant information on the root variable? This problem arose independently in biology, information theory and statistical physics. For all b, we construct a channel for which the variable at the root of the b-ary tree is independent of the configuration at the second leve...
-
作者：Koltchinskii, V; Panchenko, D; Lozano, F
作者单位：University of New Mexico; Pontificia Universidad Javeriana
摘要：A problem of bounding the generalization error of a classifier f is an element of conv(H), where H is a base class of functions (classifiers), is considered. This problem frequently occurs in computer learning, where efficient algorithms that combine simple classifiers into a complex one (such as boosting and bagging) have attracted a lot of attention. Using Talagrand's concentration inequalities for empirical processes, we obtain new sharper bounds on the generalization error of combined clas...
