Results 11 to 20 of about 932 (214)
One Value of Smoothing Parameter vs Interval of Smoothing Parameter Values in Kernel Density Estimation [PDF]
Acta Universitatis Lodziensis. Folia Oeconomica, 2017 Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error.
Aleksandra Katarzyna Baszczyńska
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New bounds of the smoothing parameter for lattices.
PLoS ONEThe smoothing parameter on lattices is crucial for lattice-based cryptographic design. In this study, we establish a new upper bound for the lattice smoothing parameter, which represents an improvement over several significant classical findings. For one-
Heng Guo +3 more
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Reservoir characterization and porosity classification using probabilistic neural network (PNN) based on single and multi-smoothing parameters [PDF]
International Journal of Mining and Geo-Engineering, 2022 A probabilistic neural network (PNN) is a feed-forward neural network using a smoothing parameter. We used the PNN algorithm based on single and multi-smoothing parameters for multi-dimensional data classification.
Masood Lashkari Ahangarani +2 more
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Histogram Filter with Smoothing Parameter Setting
Доклады Белорусского государственного университета информатики и радиоэлектроники, 2023 A histogram filter with smoothing parameter settings is discussed in the article. The histogram filter can be effectively applied in the problems of identification (recognition) of distribution laws for small amounts of data.
A. V. Ausiannikau, V. M. Kozel
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Smoothness Parameter of Power of Euclidean Norm [PDF]
Journal of Optimization Theory and Applications, 2020 AbstractIn this paper, we study derivatives of powers of Euclidean norm. We prove their Hölder continuity and establish explicit expressions for the corresponding constants. We show that these constants are optimal for odd derivatives and at most two times suboptimal for the even ones.
Rodomanov, Anton, Nesterov, Yurii
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Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
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On the Lattice Smoothing Parameter Problem [PDF]
2013 IEEE Conference on Computational Complexity, 2013 The smoothing parameter $η_ε(\mathcal{L})$ of a Euclidean lattice $\mathcal{L}$, introduced by Micciancio and Regev (FOCS'04; SICOMP'07), is (informally) the smallest amount of Gaussian noise that "smooths out" the discrete structure of $\mathcal{L}$ (up to error $ε$).
Kai-Min Chung +3 more
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Smoothing splines with varying smoothing parameter [PDF]
Biometrika, 2013 This paper considers the development of spatially adaptive smoothing splines for the estimation of a regression function with non-homogeneous smoothness across the domain. Two challenging issues that arise in this context are the evaluation of the equivalent kernel and the determination of a local penalty.
Xiao Wang, Pang Du, Jinglai Shen
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Analisis Error Terhadap Peramalan Data Penjualan
Techno.Com, 2021 Tujuan penelitian ini membahas tentang peramalan permintaan lampu LED bermerk Sanyo. Penelitian ini menggunakan metode Moving Average dan Exponential Smoothing.
Alyauma Hajjah, Yulvia Nora Marlim
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Trend-Smooth: Accelerate Asynchronous SGD by Smoothing Parameters Using Parameter Trends [PDF]
IEEE Access, 2019 Stochastic gradient descent(SGD) is the fundamental sequential method in training large scale machine learning models. To accelerate the training process, researchers proposed to use the asynchronous stochastic gradient descent (A-SGD) method in model learning.
Guoxin Cui +4 more
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