Results 11 to 20 of about 985,928 (292)
Investigation of Parametric, Non-Parametric and Semiparametric Methods in Regression Analysis
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2022 Regression analysis is known as statistical methods applied to model and analyze the relationship between variables. Regression method can be examined as parametric, non-parametric and semiparametric regression methods.The parametric regression method ...
Esra Yavuz, Mustafa Şahin
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On non‐parametric fatigue optimization
International Journal for Numerical Methods in Engineering, 2022 AbstractThe present work presents a novel approach for semi‐analytic adjoint sensitivity‐based design optimization for nonproportional fatigue damage. In order to apply fatigue damage in sensitivity‐based design optimizations, an essential part is to calculate correct sensitivities. However, this is not straight forward since fatigue damage calculation
Roman Sartorti +3 more
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Non-parametric Dependent Components [PDF]
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006 Canonical correlation analysis (CCA) is equivalent to finding mutual information-maximizing projections for normally distributed data. We remove the restriction of normality by non-parametric estimation, and formulate the problem of finding dependent components with a connection to Bayes factors. The method is applied for characterizing yeast stress by
Arto Klami, Samuel Kaski
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Bibliometric Study of the Efficiency of Public Expenditure on Education
Revista CEA, 2020 This bibliometric study analyzes the literature on the efficiency of public education expenditure using two types of bibliometric indicators: quantity (number of publications) and quality (impact by year, author, and journal).
Juliana Arias-Ciro
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Non-Parametric Calibration for Classification [PDF]
CoRR, 2019 Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification frameworks, in particular deep neural networks, achieve high accuracy, they tend to incorrectly estimate uncertainty. In this paper, we propose a method that adjusts the confidence
Wenger, Jonathan +2 more
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CoRR, 2022 Recent feed-forward neural methods of arbitrary image style transfer mainly utilized encoded feature map upto its second-order statistics, i.e., linearly transformed the encoded feature map of a content image to have the same mean and variance (or covariance) of a target style feature map.
Jeong-Sik Lee, Hyun-Chul Choi
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Nonparametric Clustering of Mixed Data Using Modified Chi-Squared Tests
Entropy, 2022 We propose a non-parametric method to cluster mixed data containing both continuous and discrete random variables. The product space of the continuous and discrete sample space is transformed into a new product space based on adaptive quantization on the
Yawen Xu, Xin Gao, Xiaogang Wang
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Non-Parametric Probabilistic Image Segmentation [PDF]
, 2007 We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well ...
Andreetto, Marco +2 more
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Frontiers in Genetics, 2019 There is a paradigm shift from the traditional focus on the “average” individual towards the definition and analysis of trait variation within individual life-history and among individuals in populations.
Joao A.N. Filipe, Ilias Kyriazakis
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Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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