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97,790
Gaussian processes for ordinal regression
 Journal of Machine Learning Research
, 2004
"... We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation ..."
Abstract

Cited by 117 (4 self)
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We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation
Transductive Ordinal Regression
"... Abstract—Ordinal regression is commonly formulated as a multiclass 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. The avail ..."
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Cited by 4 (0 self)
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Abstract—Ordinal regression is commonly formulated as a multiclass 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
Collaborative Ordinal Regression
 Proceedings of NIPS Workshop
, 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 simultaneously. Rather than modeling each task indivi ..."
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Cited by 20 (1 self)
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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 simultaneously. Rather than modeling each task
On the Consistency of Ordinal Regression Methods
"... Ordinal regression is a common supervised learning problem sharing properties with both regression and classification. Many of the ordinal regression algorithms that have been proposed can be viewed as methods that minimize a convex surrogate of the zeroone, absolute, or squared errors. We extend ..."
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Ordinal regression is a common supervised learning problem sharing properties with both regression and classification. Many of the ordinal regression algorithms that have been proposed can be viewed as methods that minimize a convex surrogate of the zeroone, absolute, or squared errors. We extend
Support vector ordinal regression
 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. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these opt ..."
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Cited by 39 (2 self)
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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. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size
Feature selection for ordinal regression
 In Proceedings of the 25th ACM Symposium on Applied Computing (SAC’10
, 2010
"... Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining commun ..."
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Cited by 8 (6 self)
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Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining
Support Vector Learning for Ordinal Regression
 In International Conference on Artificial Neural Networks
, 1999
"... We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulatio ..."
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Cited by 97 (1 self)
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We investigate the problem of predicting variables of ordinal scale. This task is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent
Collaborative Ordinal Regression
, 2016
"... 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 simultaneously. Rather than modeling each task in ..."
Abstract
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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 simultaneously. Rather than modeling each task
Ordinal regression by extended binary classification
 In
, 2007
"... We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm ..."
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Cited by 40 (5 self)
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We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification
Stereotype Ordinal Regression
, 2001
"... There are a number of reasonable approaches to analysing an ordinal outcome variable. One common approach, known as the Proportional Odds (PO) Model, is implemented in Stata as ologit. If the assumptions of the PO model are not satisfied, an alternative is to treat the outcome as categorical, rather ..."
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Cited by 4 (0 self)
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There are a number of reasonable approaches to analysing an ordinal outcome variable. One common approach, known as the Proportional Odds (PO) Model, is implemented in Stata as ologit. If the assumptions of the PO model are not satisfied, an alternative is to treat the outcome as categorical
Results 1  10
of
97,790