Results 211 to 220 of about 96,202 (258)
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 Tsang, Yew-Soon Ong, Ivor W Tsang
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.
Gerhard Tutz
exaly +2 more sources
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Collaborative ordinal regression
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006Ordinal 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 simultaneously.
Shipeng Yu +3 more
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2010
Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
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Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
openaire +1 more source
Multiple-Instance Ordinal Regression
IEEE Transactions on Neural Networks and Learning Systems, 2018Ordinal regression (OR) is a paradigm in supervised learning, which aims at learning a prediction model for ordered classes. The existing studies mainly focus on single-instance OR, and the multi-instance OR problem has not been explicitly addressed. In many real-world applications, considering the OR problem from a multiple-instance aspect can yield ...
Yanshan Xiao, Bo Liu 0002, Zhifeng Hao
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Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012Ordinal regression, which aims at determining the rating of a data item on a discrete rating scale, is an important research topic in pattern mining and multimedia data analysis. Most of the existing approaches of ordinal regression try to seek only one direction on which the projected data are well ranked.
Yang Liu 0007 +3 more
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Soft Labels for Ordinal Regression
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Ordinal regression attempts to solve classification problems in which categories are not independent, but rather follow a natural order. It is crucial to classify each class correctly while learning adequate interclass ordinal relationships. We present a simple and effective method that constrains these relationships among categories by seamlessly ...
Raul Diaz, Amit Marathe
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Ordinal Regression With Pinball Loss
IEEE Transactions on Neural Networks and Learning SystemsOrdinal regression (OR) aims to solve multiclass classification problems with ordinal classes. Support vector OR (SVOR) is a typical OR algorithm and has been extensively used in OR problems. In this article, based on the characteristics of OR problems, we propose a novel pinball loss function and present an SVOR method with pinball loss (pin-SVOR ...
Guangzheng Zhong +4 more
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Bayesian Hierarchical Ordinal Regression
2005We present a Bayesian approach to ordinal regression. Our model is based on a hierarchical mixture of experts model and performs a soft partitioning of the input space into different ranks, such that the order of the ranks is preserved. Experimental results on benchmark data sets show a comparable performance to support vector machine and Gaussian ...
Ulrich Paquet +2 more
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