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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Use of Two Smoothing Parameters in Penalized Spline Estimator for Bi-variate Predictor Non-parametric Regression Model [PDF]
Journal of Sciences, Islamic Republic of Iran, 2020 Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The
Anna Islamiyati
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PREDIKSI JUMLAH CALON PESERTA DIDIK BARU MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI BROWN
Jurnal Lebesgue, 2020 Peramalan data statistika memerlukan kesesuaian pola data dengan metode peramalan yang digunakan. Tujuan penelitian ini yaitu memprediksi jumlah mahasiswa baru pada tahun ajaran baru menggunakan metode Double Exponential Smoothing satu parameter dari ...
Aden, Anggela Supriyanti
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Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
Revstat Statistical Journal, 2021 This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz +2 more
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Stochastic Smoothing Methods for Nonsmooth Global Optimization
Кібернетика та комп'ютерні технології, 2020 . The paper presents the results of testing the stochastic smoothing method for global optimization of a multiextremal function in a convex feasible subset of Euclidean space.
V.I. Norkin
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A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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Journal of Open Innovation: Technology, Market and Complexity, 2023 This research concentrates on using neural networks in the modelling and prediction of macroeconomic variables in specific. Macroeconomic predictors are particularly interested in neural networks because of their capacity to predict any linear or non ...
Rabia Sabri +5 more
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Resistant Nonparametric Smoothing with S-PLUS
Journal of Statistical Software, 2004 In this paper we introduce and illustrate the use of an S-PLUS set of functions to fit M-type smoothing splines with the smoothing parameter chosen by a robust criterion (either a robust version of cross-validation or a robust version of Mallows's Cp ...
Eva Cantoni
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Penalized spline estimator with multi smoothing parameters in bi-response multi-predictor nonparametric regression model for longitudinal data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2020 Penalized spline estimators that depend on a smoothing parameter is one type of estimator used in the estimation regression curve in nonparametric regression.
Anna Islamiyati, Fatmawati, Nur Chamidah
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