Results 71 to 80 of about 40,843 (95)
An explicit version of the Chebyshev-Markov-Stieltjes inequalities and its applications [PDF]
core +1 more source
Convex formulation and global optimization for multimodal active contour segmentation [PDF]
De Vylder, Jonas +2 more
core
Improved Fourier descriptors for 2-D shape representation [PDF]
De Vylder, Jonas
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Parisian ruin probability - the De Vylder type approximation
Summary: The Parisian ruin occurs as the capital of the insurance company is negative longer than a predefined period of time. In this article, we propose a simple and fast technique for calculating the Parisian ruin probability for the Cramér-Lundberg model with arbitrary claims that have the first three moments finite.
Zdeb, Martyna, Teuerle, Marek A.
semanticscholar +3 more sources
De Vylder type approximation of the ruin probability for the insurer-reinsurer model
Summary: In this article we introduce a De Vylder type of approximation of the ruin probability for a two-dimensional risk process, where claims and premiums are shared with a predetermined proportion. Such a process is usually associated with the insurer-reinsurer model.
Burnecki, Krzysztof +2 more
semanticscholar +5 more sources
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Avram, F., Banik, A. D., Horvath, A.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Xiang, Duan, Baige, Zhang, Lianzeng
semanticscholar +4 more sources
A new De Vylder type approximation of the ruin probability in infinite time [PDF]
In this paper we introduce a generalization of the De Vylder approximation. Our idea is to approximate the ruin probability with the one for a different process with gamma claims, matching first four moments. We compare the two approximations studying mixture of exponentials and lognormal claims.
Krzysztof Burnecki +2 more
core +3 more sources
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IEEE transactions on industrial electronics (1982. Print), 2022
The sigmoid function is a widely used nonlinear activation function in neural networks. In this article, we present a modular approximation methodology for efficient fixed-point hardware implementation of the sigmoid function.
Zhe Pan +4 more
semanticscholar +1 more source
The sigmoid function is a widely used nonlinear activation function in neural networks. In this article, we present a modular approximation methodology for efficient fixed-point hardware implementation of the sigmoid function.
Zhe Pan +4 more
semanticscholar +1 more source

