Results 261 to 270 of about 109,915 (302)

Multiple-Instance Ordinal Regression

IEEE Transactions on Neural Networks and Learning Systems, 2018
Ordinal 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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

Ordinal regression for interaction quality prediction

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The automatic prediction of the quality of a dialogue is useful to keep track of a spoken dialogue system's performance and, if necessary, adapt its behaviour. Classifiers and regression models have been suggested to make this prediction. The parameters of these models are learnt from a corpus of dialogues evaluated by users or experts.
El Asri, Layla, Khouzaimi, Hatim, Laroche, Romain, Pietquin, Olivier   +3 more
openaire   +2 more sources

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.
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

Regression Models for Ordinal Outcomes

JAMA, 2022
Benjamin, French, Matthew S, Shotwell
openaire   +2 more sources

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.
openaire   +1 more source