scholarly journals Average sampling in certain subspaces of Hilbert–Schmidt operators on $$L^2(\mathbb {R}^d)$$

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Antonio G. García
Keyword(s):  
Average Sampling

Truncation Error Analysis on Reconstruction of Signal From Unsymmetrical Local Average Sampling

2015 ◽  
Vol 45 (10) ◽  
pp. 2100-2104 ◽  
Author(s):  
Yanwei Pang ◽  
Zhanjie Song ◽  
Xuelong Li ◽  
Jing Pan
Keyword(s):  
Error Analysis ◽  
Truncation Error ◽  
Average Sampling ◽  
Local Average

scholarly journals A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guo Yangyudongnanxin
Keyword(s):  
Social Relationship ◽  
Search Algorithm ◽  
Classification Model ◽  
The Internet ◽  
Identification Algorithm ◽  
Association Model ◽  
Intelligent Search ◽  
Average Sampling ◽  
Customer Classification ◽  
Customer Identification

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

scholarly journals Average sampling in mixed shift-invariant subspaces with generators in hybrid-norm spaces

2021 ◽  
Author(s):  
Haizhen Li ◽  
Yan Tang
Keyword(s):  
Exponential Convergence ◽  
Invariant Subspaces ◽  
Time Varying ◽  
Projection Algorithms ◽  
Iterative Approximation ◽  
Wiener Amalgam Space ◽  
Average Sampling ◽  
Norm Space ◽  
Sampling And Reconstruction ◽  
Mixed Lebesgue Spaces

This paper mainly studies the average sampling and reconstruction in shift-invariant subspaces of mixed Lebesgue spaces $L^{p,q}(\mathbb{R}^{d+1})$, under the condition that the generator $\varphi$ of the shift-invariant subspace belongs to a hybrid-norm space of mixed form, which is weaker than the usual assumption of Wiener amalgam space and allows to control the orders $p,q$. First, the sampling stability for two kinds of average sampling functionals are established. Then, we give the corresponding iterative approximation projection algorithms with exponential convergence for recovering the time-varying shift-invariant signals from the average samples.

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