Results 11 to 20 of about 259,894 (334)
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
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Distributed Ordinal Regression Over Networks [PDF]
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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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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Incremental Sparse Bayesian Ordinal Regression [PDF]
Neural Networks, 2018 Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that ...
de Rijke, Maarten, Li, Chang
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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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Penalized Regression with Ordinal Predictors [PDF]
International Statistical Review, 2008 Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this article penalized regression techniques are proposed.
Gertheiss, Jan, Tutz, Gerhard
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Transductive Ordinal Regression [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2012 Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed.
Chun-Wei Seah +2 more
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Regularized Ordinal Regression and the ordinalNet R Package [PDF]
, 2017 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.
Hanlon, Bret M. +2 more
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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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Ordinal logistic regression [PDF]
American Journal of Orthodontics and Dentofacial Orthopedics, 2017 Despina Koletsi, Nikolaos Pandis
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