Results 211 to 220 of about 544,201 (272)

Involvement of executive resources in prediction: Effects of load and hearing loss in older adults. [PDF]

Psychon Bull Rev
Fernandez LB, Shehzad M, Hadley LV.
europepmc   +1 more source

Correlation cluster analysis of slope safety monitoring data in reservoir areas. [PDF]

PLoS One
Yang W, Li T, Wen B, Ren Y.
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New insights into the molecular basis of gametogenesis in the hybridogenetic water frog Pelophylax esculentus. [PDF]

Sci Rep
Plötner M, Meixner M, Poustka AJ, Grau JH, Theusner S, Liere K, Schüllermann T, Doležálková-Kaštánková M, Choleva L, Plötner J.   +9 more
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Rapid and reproducible haplotyping of complete mitochondrial genomes using split k-mers. [PDF]

BMC Genomics
Stuehler DS, Cano LM, Heck M.
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A wide diversity of viruses detected in African mammals involved in the wild meat supply chain. [PDF]

PLoS Pathog
Geraerts M, Gombeer S, Nebesse C, Akaibe D, Akaibe D, Baelo P, Chaber AL, Gaubert P, Gembu GC, Joffrin L, Laudisoit A, Laurent N, Leirs H, Mande C, Mariën J, Ngoy S, Těšíková J, Vanderheyden A, van Vredendaal R, Verheyen E, Gryseels S.   +20 more
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Sensor Bias Estimation Based on Ridge Least Trimmed Squares

IEEE Transactions on Aerospace and Electronic Systems, 2020
A robust sensor bias estimation approach, named as the ridge least trimmed squares (RLTS), is proposed. Combing the advantages of ridge regression and least trimmed squares, RLTS can solve the sensor bias estimation problem with the presence of misassociations and ill-conditioning. Simulation results verify the effectiveness of the proposed approach.
Wei Tian, Xuebao Wang
exaly   +3 more sources

Partial least trimmed squares regression

Chemometrics and Intelligent Laboratory Systems, 2022
Zhonghao Xie
exaly   +3 more sources

On least trimmed squares neural networks

Neurocomputing, 2015
In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS neural networks for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers.
Yih-Lon Lin, Jer-Guang Hsieh, Jyh-Horng Jeng   +2 more
exaly   +2 more sources