Results 31 to 40 of about 1,589,421 (354)
Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
Algorithms, 2022 We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing.
Diego Granziol +5 more
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Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2004 Kernel smoothing provides a simple way for finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model and a random design regression model.
Jitka Poměnková
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Adversarially Robust Kernel Smoothing
, 2021 We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy.
Zhu, Jia-Jie +3 more
openaire +4 more sources
E-Jurnal Matematika, 2018 Nonparametric regression can be applied for some data types one of them is time series data. The technique of this method is called smoothing technique.
DEWA AYU DWI ASTUTI +2 more
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Kernel Density Estimators for Gaussian Mixture Models
Lithuanian Journal of Statistics, 2013 The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
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Effects of spatial smoothing on group-level differences in functional brain networks
Network Neuroscience, 2020 Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations.
Ana María Triana +3 more
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Nonparametric estimate remarks
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2006 Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model.
Jitka Poměnková
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Uniform error bounds for smoothing splines [PDF]
, 2006 Almost sure bounds are established on the uniform error of smoothing spline estimators in nonparametric regression with random designs. Some results of Einmahl and Mason (2005) are used to derive uniform error bounds for the approximation of the spline ...
Eggermont, P. P. B., LaRiccia, V. N.
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Peramalan Permintaan Inti Sawit (Kernel) di PT. Perkebunan Nusantara V Sei Pagar
Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri, 2018 Produksi inti sawit (kernel) yang berlebih di PT. Perkebunan Nusantara V Sei Pagar mengindikasikan produksi yang tidak direncanakan dengan baik. Akibatnya terdapat penumpukan sisa penjualan selama tahun 2016 yang jumlahnya mengalami kenaikan 2 kali lipat
Nofirza Nofirza
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Regression with Ordered Predictors via Ordinal Smoothing Splines
Frontiers in Applied Mathematics and Statistics, 2017 Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables ...
Nathaniel E. Helwig, Nathaniel E. Helwig
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