Divisible convex cones constituted by hunt convolution kernels

1982 ◽  
pp. 204-226
Author(s):  
Masayuki Itô
Keyword(s):  
Convex Cones ◽  
Convolution Kernels

Finite dimensional locally convex cones

Filomat ◽  
2017 ◽  
Vol 31 (16) ◽  
pp. 5111-5116
Author(s):  
Davood Ayaseha
Keyword(s):  
Finite Dimension ◽  
Convex Cones ◽  
Vector Spaces ◽  
Topological Vector Spaces ◽  
Euclidean Topology ◽  
Finite Dimensional ◽  
Dimensional Cone ◽  
Locally Convex Cones ◽  
Locally Convex ◽  
Special Case

We study the locally convex cones which have finite dimension. We introduce the Euclidean convex quasiuniform structure on a finite dimensional cone. In special case of finite dimensional locally convex topological vector spaces, the symmetric topology induced by the Euclidean convex quasiuniform structure reduces to the known concept of Euclidean topology. We prove that the dual of a finite dimensional cone endowed with the Euclidean convex quasiuniform structure is identical with it?s algebraic dual.

scholarly journals Sobolev inequalities with jointly concave weights on convex cones

2020 ◽  
Author(s):  
Zoltán M. Balogh ◽  
Cristian E. Gutiérrez ◽  
Alexandru Kristály
Keyword(s):  
Convex Cones ◽  
Sobolev Inequalities

scholarly journals Fixed points in convex cones

2017 ◽  
Vol 4 (3) ◽  
pp. 68-93
Author(s):  
Nicolas Monod
Keyword(s):  
Fixed Points ◽  
Convex Cones

Normality and Nuclearity of Convex Cones

Positivity ◽  
2007 ◽  
Vol 11 (3) ◽  
pp. 485-495
Author(s):  
Fatimetou mint El Mounir
Keyword(s):  
Convex Cones

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics

2015 ◽  
Vol 41 (1) ◽  
pp. 165-173 ◽  
Author(s):  
Fabio Massimo Zanzotto ◽  
Lorenzo Ferrone ◽  
Marco Baroni
Keyword(s):  
Distributional Semantics ◽  
Important Class ◽  
Strong Link ◽  
Composition Methods ◽  
Convolution Kernels ◽  
Composition Operations ◽  
Compositional Distributional Semantics

Distributional semantics has been extended to phrases and sentences by means of composition operations. We look at how these operations affect similarity measurements, showing that similarity equations of an important class of composition methods can be decomposed into operations performed on the subparts of the input phrases. This establishes a strong link between these models and convolution kernels.

Faces and duality in convex cones

1978 ◽  
Vol 6 (3) ◽  
pp. 161-169 ◽  
Author(s):  
George Phillip Barker
Keyword(s):  
Convex Cones
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