Results 251 to 260 of about 264,683 (292)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +1 more source
A Metric Approach for Ordinal Regression
1997This paper presents a metric approach for the regression of ordinal variables. In contrast to most other studies, the problem of independent, ordinal variables with a dependent variable that is a metric scale is analyzed. For this situation, some properties of the estimated parameters of the model are described.
openaire +1 more source
Ordinal Regression with Sparse Bayesian
2009In this paper, a probabilistic framework for ordinal prediction is proposed, which can be used in modeling ordinal regression. A sparse Bayesian treatment for ordinal regression is given by us, in which an automatic relevance determination prior over weights is used. The inference techniques based on Laplace approximation is adopted for model selection.
Xiao Chang, Qinghua Zheng, Peng Lin
openaire +1 more source
Regression Models for Ordinal Outcomes
JAMA, 2022Benjamin, French, Matthew S, Shotwell
openaire +2 more sources
Ordinal Regression and Ranking
2011Accurate ordering or ranking over instances is of paramount importance for several applications (Faria et al. Learning to rank for content-based image retrieval. In: Proceedings of the Multimedia Information Retrieval Conference, pp. 285–294, 2010; Veloso et al. Learning to rank at query-time using association rules.
Adriano Veloso, Wagner Meira
openaire +1 more source
Ordinal Regression in Evolutionary Computation
2006Surrogate ranking in evolutionary computation using ordinal regression is introduced. The fitness of individual points is indirectly estimated by modeling their rank. The aim is to reduce the number of costly fitness evaluations needed for evolution.
openaire +1 more source
2010
In this chapter, the standard logistic model is extended to handle outcome variables that have more than two ordered categories. When the categories of the outcome variable have a natural order, ordinal logistic regression may be appropriate.
David G. Kleinbaum, Mitchel Klein
openaire +1 more source
In this chapter, the standard logistic model is extended to handle outcome variables that have more than two ordered categories. When the categories of the outcome variable have a natural order, ordinal logistic regression may be appropriate.
David G. Kleinbaum, Mitchel Klein
openaire +1 more source
Relative margin induced support vector ordinal regression
Expert Systems With Applications, 2023Fa Zhu, Xingchi Chen, Shuo Chen
exaly