Results 211 to 220 of about 2,640,523 (250)

Neighborhood preserving ordinal regression

Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012
Ordinal 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, Yan Liu 0004, Keith C. C. Chan, Jing Zhang 0010   +3 more
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Soft Labels for Ordinal Regression

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Ordinal 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 Systems
Ordinal 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, Yanshan Xiao, Bo Liu 0002, Liang Zhao, Xiangjun Kong   +4 more
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Bayesian Hierarchical Ordinal Regression

2005
We 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, Sean B. Holden, Andrew Naish-Guzman   +2 more
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Ordinal Regression with Sparse Bayesian

2009
In 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
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Regression Models for Ordinal Outcomes

JAMA, 2022
Benjamin, French, Matthew S, Shotwell
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A Metric Approach for Ordinal Regression

1997
This 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.
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Ordinal Regression in Evolutionary Computation

2006
Surrogate 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.
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Regression for Ordinal Data on the Simplex

We introduce a regression framework for ordinal compositional data that operates directly on the simplex and avoids the use of log-ratio transformations. The proposed model describes the relationship between compositional predictors and ordinal compositional responses through a column-stochastic linear operator, ensuring that fitted values remain valid
Nicola Piras, Beniamino Cappelletti Montano, Monica Musio   +2 more
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Ordinal Regression and Ranking

2011
Accurate 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
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