Results 11 to 20 of about 2,640,523 (250)
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.
Ivor Wai-Hung Tsang +2 more
exaly +7 more sources
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.
Paul J Rathouz, Bret M Hanlon
exaly +4 more sources
Support Vector Ordinal Regression [PDF]
Neural Computation, 2007 In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
Wei Chu, S. Sathiya Keerthi
openaire +4 more sources
Convolutional Ordinal Regression Forest for Image Ordinal Estimation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different ...
Haiping Zhu, Fei-Yue Wang, Hongming Shan
exaly +5 more sources
Penalized Regression With Ordinal Predictors [PDF]
International Statistical Review, 2009 SummaryOrdered categorial predictors are a common case in regression modelling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this paper, existing methods are reviewed and the use of penalized regression techniques is proposed.
Jan Gertheiss, Gerhard Tutz
exaly +2 more sources
Mvord: An R Package for Fitting Multivariate Ordinal Regression Models
Journal of Statistical Software, 2020 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.
Kurt Hornik, Laura Vana
exaly +3 more sources
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
doaj +3 more sources
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
doaj +1 more source
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
doaj +1 more source
Ordinal Regression Model of Parking Search Time
Promet (Zagreb), 2023 Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic.
Jelena Simićević +2 more
doaj +1 more source