Results 21 to 30 of about 1,045,360 (258)
Efficient Cross-Validation in ILP [PDF]
, 2001 Cross-validation is a technique used in many different machine learning approaches. Straightforward implementation of this technique has the disadvantage of causing computational overhead. However, it has been shown that this overhead often consists of redundant computations, which can be avoided by performing all folds of the cross-validation in ...
Struyf, Jan, Blockeel, Hendrik
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A Comparison of Forecasting Mortality Models Using Resampling Methods
Mathematics, 2020 The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products.
David Atance, Ana Debón, Eliseo Navarro
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A theory of cross-validation error [PDF]
Journal of Experimental & Theoretical Artificial Intelligence, 1994 This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these conflicting demands must be balanced, in
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Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China
应用气象学报, 2020 Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR).
Liu Boqi, Zhu Congwen
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Compressed Sensing With Cross Validation [PDF]
IEEE Transactions on Information Theory, 2009 Compressed Sensing decoding algorithms can efficiently recover an N dimensional real-valued vector x to within a factor of its best k-term approximation by taking m = 2klog(N/k) measurements y = Phi x. If the sparsity or approximate sparsity level of x were known, then this theoretical guarantee would imply quality assurance of the resulting compressed
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Bandwidth Selection in Nonparametric Regression with Large Sample Size
Proceedings, 2018 In the context of nonparametric regression estimation, the behaviour of kernel methods such as the Nadaraya-Watson or local linear estimators is heavily influenced by the value of the bandwidth parameter, which determines the trade-off between bias and ...
Daniel Barreiro-Ures +2 more
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Improving the accuracy of classification algorithms for inductive learning rules using wrapper methods [PDF]
Tehnika, 2015 In this paper we investigate the problem of the accuracy of classifier using wrapper methods. For the purposes of classification is used a large number of algorithms: IBK, Naïve Bayes, SVM, J48 decision tree and RBF networks.
Novaković Jasmina Đ.
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JTAM (Jurnal Teori dan Aplikasi Matematika), 2022 Grade point average (GPA) is initial information for supervisors to characterize their supervised students. One model that can be used to predict a student's study period based on GPA is a machine learning-based regression model so that supervisors can ...
Sri Nurdiati, Mohamad Khoirun Najib
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Procrustes Cross-Validation — a Bridge Between Cross-Validation and Independent Validation Set [PDF]
Analytical Chemistry, 2020 In this paper we propose a new approach for validation of chemometric models. It is based on k-fold cross-validation algorithm, but, in contrast to conventional cross-validation, our approach makes possible to create a new dataset, which carries sampling uncertainty estimated by the cross-validation procedure. This dataset, called
Sergey Kucheryavskiy +3 more
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Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data
Frontiers in Future Transportation, 2021 An essential step in agent-based travel demand models is the characterization of the population, including transport-related attributes. This study looks deep into various mobility data in the province of Liège, Belgium.
Suxia Gong +7 more
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