Titlebook: Intelligent Systems; 11th Brazilian Confe João Carlos Xavier-Junior,Ricardo Araújo Rios Conference proceedings 2022 The Editor(s) (if appli

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书目名称Intelligent Systems影响因子(影响力)




书目名称Intelligent Systems影响因子(影响力)学科排名




书目名称Intelligent Systems网络公开度




书目名称Intelligent Systems网络公开度学科排名




书目名称Intelligent Systems被引频次




书目名称Intelligent Systems被引频次学科排名




书目名称Intelligent Systems年度引用




书目名称Intelligent Systems年度引用学科排名




书目名称Intelligent Systems读者反馈




书目名称Intelligent Systems读者反馈学科排名

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Ângelo Gregório Lovatto,Leliane Nunes de Barros,Denis D. Mauáundles. In Sec. 1 we shall give the basic definitions of the Hermitian analogues of the classical concepts of (Riemannian) metric, connection, and curvature. This is carried out in the context of differentiable C-vector bundles over a differentiable manifold . More specific formulas are obtained in

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Arnaldo Vitor Barros da Silva,Luis Filipe Alves Pereiraolds. In Sec. 1 we shall present a discussion of the exterior algebra on a Hermitian vector space, introducing the fundamental 2-form and the Hodge *-operator associated with the Hermitian metric. In Sec. 2 we shall discuss and prove the principal results concerning harmonic forms on compact manifol

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Igor Felipe de Camargo,Rodolfo Stoffel Antunes,Gabriel de O. Ramoss likely to need. The techniques described are presented with due regard for their theoretical basis; but the emphasis is on detailed discussion of the ideas of the differ­ ential calculus and on the avoidance of false statements rather than on complete proofs of all results. It is a frequent experi

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explicit

,Least-Squares Linear Dilation-Erosion Regressor Trained Using a Convex-Concave Procedure,a convex combination of the composition of linear and morphological operators. They yield continuous piecewise linear functions and, thus, are universal approximators. Besides introducing the .-DER model, we formulate their training as a difference of convex (DC) programming problem. Precisely, an .

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,Exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study,n used to select a policy that maximizes the objective function. Stochastic Value Gradient (SVG) methods perform the latter step by optimizing some estimate of the value function gradient. Despite showing promising empirical results, many implementations of SVG methods lack rigorous theoretical or e

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