Results 11 to 20 of about 2,609,891 (296)
Regularized Ordinal Regression and the ordinalNet R Package [PDF]
Journal of Statistical Software, 2021 Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection.
Michael J. Wurm +2 more
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Distributed Support Vector Ordinal Regression over Networks [PDF]
Entropy, 2022 Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization.
Huan Liu, Jiankai Tu, Chunguang Li
doaj +2 more sources
Ordinal regression increases statistical power to predict epilepsy surgical outcomes [PDF]
Epilepsia Open, 2022 Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing ...
Adam S. Dickey +2 more
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Collaborative Ordinal Regression
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled ...
Shipeng Yu, Volker Tresp, Kai Yu
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Support vector ordinal regression [PDF]
Neural Computation, 2007 In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales.
Wei Chu, S. Sathiya Keerthi
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Predicting progression of Alzheimer's disease using ordinal regression. [PDF]
PLoS ONE, 2014 We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD ...
Orla M Doyle +10 more
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Geographically Weighted Probit Ordinal Regression Model Estimation [PDF]
E3S Web of ConferencesGeographically Weighted Probit Ordinal Regression (GWPOR) is a combined method between Geographically Weighted Regression and Probit Ordinal Regression. This study estimates the percentage of poor people using the GWPOR method.
Kurniawan Muh. Idham +2 more
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ordinalgmifs: An R Package for Ordinal Regression in High-dimensional Data Settings [PDF]
Cancer Informatics, 2014 High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal < pre-malignant < malignant or by stage of cancer using a small set of molecular features.
Kellie J. Archer +5 more
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Distributed Ordinal Regression Over Networks
IEEE Access, 2021 Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns.
Huan Liu, Jiankai Tu, Chunguang Li
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ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA
Barekeng, 2022 Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability.
Jajang Jajang +2 more
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