Results 1 to 10 of about 76,031 (307)
New bounds of the smoothing parameter for lattices. [PDF]
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-dimensional integer lattices, under specific and optimized conditions, we have achieved a more ...
Guo H, Liu F, Wang L, Tian K.
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Resistant Selection of the Smoothing Parameter for Smoothing Splines
Statistics and Computing, 2001 Robust automatic selection techniques for the smoothing parameter of a smoothing spline are introduced. They are based on a robust predictive error criterion and can be viewed as robust versions of C p and cross-validation. They lead to smoothing splines
Ronchetti, Elvezio, Cantoni, Eva
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Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy. [PDF]
Comput Stat Data Anal, 2010 Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. We compare various robust smoothing methods for estimating fluorescence emission spectra and data driven methods for the ...
Lee JS, Cox DD.
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A note on smoothing parameter selection for penalized spline smoothing
Journal of Statistical Planning and Inference, 2005 Kauermann G. A note on smoothing parameter selection for penalized spline smoothing. JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2005;127(1-2):53-69.In nonparametric regression the smoothing parameter can be selected by minimizing a Mean Squared Error
Kauermann, Göran
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Smoothing Parameter Selection for Nonparametric Regression Using Smoothing Spline
, 2013 WOS: 000214937300008In this paper, the smoothing parameter selection problem has been examined in respect to a smoothing spline implementation in predicting nonparametric regression models. For this purpose, a simulation study has been performed by using
Aydin, Dursun +2 more
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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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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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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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Smoothing Parameter and Model Selection for General Smooth Models [PDF]
Journal of the American Statistical Association, 2016 This paper discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to ...
Wood, Simon N. +2 more
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