Smoothing
In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points higher than the adjacent points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. Smoothing may be used in two important ways that can aid in data analysis (1) by being able to extract more information from the data as long as the assumption of smoothing is reasonable and (2) by being able to provide analyses that are both flexible and robust. Many different algorithms are used in smoothing.
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Adaptive smootheningAdaptive smoothingAdditive modelAdditive smoothingAlgorithms for smoothingBeta distributionBias–variance tradeoffBilateral filterCharles Sanders PeirceChronuxCurve fittingData reductionData smoothingDegrees of freedom (statistics)Delaunay tessellation field estimatorDiscrete Universal DenoiserDiscretizationFilter (signal processing)Filtering problem (stochastic processes)Founders of statisticsFunctional additive modelsGeneralized functional linear modelGeneralized linear modelGrace WahbaGraph cuts in computer visionHistory of scientific methodImage-based meshingImageJIndex of graphonomics-related articlesKanade–Lucas–Tomasi feature trackerKernel density estimationKneser–Ney smoothingLabPlotLaplacian smoothingList of United States graduate business school rankingsList of statistics articlesLulu smoothingMauna_KeaMean squared prediction errorMedian filter
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Smoothing
In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal are modified so individual points higher than the adjacent points (presumably because of noise) are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. Smoothing may be used in two important ways that can aid in data analysis (1) by being able to extract more information from the data as long as the assumption of smoothing is reasonable and (2) by being able to provide analyses that are both flexible and robust. Many different algorithms are used in smoothing.
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En estadística i processament ...... la representació espai-escala.
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En estadística y procesamiento ...... representación espacio-escala.
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In statistica ed elaborazione ...... dell'evidenziamento dei bordi.
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In statistics and image proces ...... tion to obtain the 'best' fit.
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Le lissage est une technique q ...... e régression non paramétrique.
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Utjämning eller glättning är i ...... . De är med andra ord kausala.
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在统计学和图像处理中,通过建立近似函数尝试抓住数据中的主要模 ...... 法可用于平滑。数据平滑通常通过最简单的密度估计或直方图完成。
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En estadística i processament ...... la representació espai-escala.
@ca
En estadística y procesamiento ...... representación espacio-escala.
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In statistica ed elaborazione ...... (2D, 3D, 4D) oppure nel tempo.
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In statistics and image proces ...... orithms are used in smoothing.
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Le lissage est une technique q ...... e régression non paramétrique.
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Utjämning eller glättning är i ...... kt mest välgrundade metodiken.
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在统计学和图像处理中,通过建立近似函数尝试抓住数据中的主要模 ...... 法可用于平滑。数据平滑通常通过最简单的密度估计或直方图完成。
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label
Alisado
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Glätten (Mathematik)
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Lisciamento
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Lissage (mathématiques)
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Smoothing
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Suavitzat
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Utjämning
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平滑
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