Results 281 to 290 of about 5,940,643 (309)

Lower-Rank Tensor Approximation and Multiway Filtering

SIAM Journal on Matrix Analysis and Applications, 2008
This paper presents some recent filtering methods based on the lower-rank tensor approximation approach for denoising tensor signals. In this approach, multicomponent data are represented by tensors, that is, multiway arrays, and the presented tensor filtering methods rely on multilinear algebra.
Muti, Damien, Bourennane, Salah, Marot, Julien   +2 more
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Interpretations of Lower Approximations in Inclusion Degrees

2016
The nature of uncertainty inference is to give evaluations on inclusion relationships by means of various measures. In this paper we introduce the concept of inclusion degrees into rough set theory. It is shown that the lower approximations of the rough set theory in both the crisp and the fuzzy environments can be represented as inclusion degrees.
Wei-Zhi Wu 0001, Chao-Jun Chen, Xia Wang
openaire   +1 more source

Cutting Plane Algorithms and Approximate Lower Subdifferentiability

Journal of Optimization Theory and Applications, 2011
This paper introduced and investigated a notation of approximate lower subdifferentiability. It used the fact that a conjugation of the Fenchel-Moreau type can be introduced for quasiconvex functions using quasi-affine functions or the coupling functional. This paper intends to use this concept to develop cutting plane algorithms.
Jean-Paul Penot, Pham Hong Quang
openaire   +2 more sources

Lower Bounds for $$n$$ -Term Approximations

Mathematical Notes, 2001
Let \(X\) be a real normed space, \(\Phi\subset X\) a subset in \(X\) (a dictionary), and \(f\in X\). The \(n\)-term approximation of an element \(f\) with respect to the dictionary \(\Phi\) is defined as \(e_n(f,\Phi,X)\equiv\inf_{P\in\Sigma_n}\| f-P\|_X\), \(\Sigma_n\equiv\left\{\sum_{j=1}^na_jx_j,a_j\in\mathbb R,x_j\in\Phi\right\}\), \(n\in\mathbb N\
openaire   +2 more sources

An Attainable Lower Bound for the Best Normal Approximation

SIAM Journal on Matrix Analysis and Applications, 1994
It is shown that the distance between a complex matrix and the set of normal matrices (using the Frobenius norm) is bounded by the departure from normality , defined by Henrici, from above and by the same term divided by the square root of \(n\), from below. A sharper bound is obtained using Schur decomposition.
openaire   +3 more sources

Lower Bounds, Snapshots and Approximation

1997
IN SEARCH of efficient fault-tolerant algorithms, it is important to also consider the lower bounds for relevant problems. The efficiency achieved by a fault-tolerant Write-All algorithm in a particular parallel model needs to be contrasted with the lower bound results for the model.
Paris Christos Kanellakis, Alex Allister Shvartsman   +1 more
openaire   +1 more source

Approximating the Rainbow – Better Lower and Upper Bounds

2012
In this paper we study the minimum rainbow subgraph problem, motivated by applications in bioinformatics. The input of the problem consists of an undirected graph where each edge is coloured with one of the p possible colors. The goal is to find a subgraph of minimum order (i.e.
openaire   +4 more sources

Networks for Approximation and Learning

Proceedings of the IEEE, 1990
Federico Girosi
exaly  

Approximation Capabilities of Multilayer Feedforward Networks

Neural Networks, 1991
Kurt Hornik
exaly  

A Survey-Cum-Tutorial on Approximations to Gaussian Q Function for Symbol Error Probability Analysis Over Nakagami- m Fading Channels

IEEE Communications Surveys and Tutorials, 2019
Supriya Aggarwal
exaly