نتایج جستجو برای: random parameter values

تعداد نتایج: 939263  

Journal: :Taiwanese Journal of Mathematics 2007

Journal: :CoRR 2017
Anthony J. Bagnall Gavin C. Cawley

We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems. This means that fair and rigorous evaluation of...

Journal: :Transactions of the American Mathematical Society 2020

Journal: :Mathematics 2022

Given a sequence (Xn) of random variables, Xn is said to be near-record if Xn∈(Mn−1−a,Mn−1], where Mn=max{X1, …, Xn} and a>0 parameter. We investigate the point process η on [0,∞) values from an integer-valued, independent identically distributed sequence, showing that it Bernoulli cluster process. derive probability generating functional formulas for expectation, variance covariance countin...

Journal: :Stochastic Processes and their Applications 1990

Journal: :Physical review. E, Statistical, nonlinear, and soft matter physics 2002
Takahisa Harayama Rainer Klages Pierre Gaspard

We propose a flower-shaped billiard in order to study the irregular parameter dependence of chaotic normal diffusion. Our model is an open system consisting of periodically distributed obstacles in the shape of a flower, and it is strongly chaotic for almost all parameter values. We compute the parameter dependent diffusion coefficient of this model from computer simulations and analyze its fun...

2012
Oliver Jeromin Marios S Pattichis Vince D Calhoun

BACKGROUND Compressive sensing can provide a promising framework for accelerating fMRI image acquisition by allowing reconstructions from a limited number of frequency-domain samples. Unfortunately, the majority of compressive sensing studies are based on stochastic sampling geometries that cannot guarantee fast acquisitions that are needed for fMRI. The purpose of this study is to provide a co...

Journal: :Discrete Applied Mathematics 2014
Guillem Perarnau Oriol Serra

The tree–depth is a parameter introduced under several names as a measure of sparsity of a graph. We compute asymptotic values of the tree–depth of random graphs. For dense graphs, p n−1, the tree–depth of a random graph G is a.a.s. td(G) = n − O( √ n/p). Random graphs with p = c/n, have a.a.s. linear tree–depth when c > 1, the tree–depth is Θ(log n) when c = 1 and Θ(log log n) for c < 1. The r...

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