Results 41 to 50 of about 96,913 (258)
Robust Estimation and Filtering Methods for Ordinal Label Noise [PDF]
Large-scale labeled datasets inevitably contain label noise,which limits the generalization performance of the model to some extent.The labels of ordinal regression datasets are discrete values,but there exist ordinal relationships between different ...
JIANG Gaoxia, WANG Fei, XU Hang, WANG Wenjian
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Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response
Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher ...
Dler H. Kadir, Ameera W. Omer
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Sparse Ordinal Logistic Regression and Its Application to Brain Decoding
Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity.
Emi Satake +4 more
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Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution
We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework.
Nemanja Petrovic +3 more
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mvord: An R Package for Fitting Multivariate Ordinal Regression Models
The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link.
Rainer Hirk, Kurt Hornik, Laura Vana
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Ordinal Regression as Structured Classification
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels. The net effect of this is to transform the underlying problem from an ordinal regression task to a (structured) classification task which we solve with conditional random fields, thereby achieving a ...
Niall Twomey +3 more
openaire +2 more sources
A constrained regression model for an ordinal response with ordinal predictors [PDF]
A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The proportional odds model (McCullagh (1980)) is used for the ordinal response, and constrained maximum likelihood estimation is used to account for the ordinality of covariates ...
Espinosa, Javier, Hennig, Christian
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ABSTRACT Objective This analysis evaluates the effect of successful reperfusion on functional outcomes after MT, stratified by admission National Institutes of Health Stroke Scale (NIHSS) and Alberta Stroke Program Early CT Score (ASPECTS) as surrogates for clinical‐core mismatch, using multicenter registry data.
Felix Schlicht +53 more
wiley +1 more source
Regression to Classification: Ordinal Prediction of Calcified Vessels Using Customized ResNet50
A substantial percentage of women die from cardiovascular disease (CVD). Computed tomography (CT) scan helps predict/monitor CVD-related diseases. However, previous studies found relation to assessing the risk of CVD by estimating severity of breast ...
Hosna Asma-Ull, Il Dong Yun, Bo La Yun
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ABSTRACT Objective Intravenous thrombolysis (IVT) before thrombectomy for ischemic stroke may alter clot structure and procedural performance. We investigated how IVT relates to thrombectomy metrics across stroke etiologies. Methods We performed a time‐to‐event analysis of consecutive patients with anterior circulation large vessel occlusion (acLVO ...
Annahita Sedghi +8 more
wiley +1 more source

