Results 291 to 300 of about 5,941,658 (334)

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

Bayesian ordinal regression for multiple criteria choice and ranking

European Journal of Operational Research, 2021
We propose a novel Bayesian Ordinal Regression approach for multiple criteria choice and ranking problems. It employs an additive value function model to represent indirect Decision Maker’s (DM’s) preferences in the form of pairwise comparisons of ...
Zice Ru, Jia-Peng Liu, M. Kadziński, Xiu-Wu Liao   +3 more
semanticscholar   +1 more source

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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Teach CLIP to Develop a Number Sense for Ordinal Regression

European Conference on Computer Vision
Ordinal regression is a fundamental problem within the field of computer vision, with customised well-trained models on specific tasks. While pre-trained vision-language models (VLMs) have exhibited impressive performance on various vision tasks, their ...
Yao Du, Qiang Zhai, Weihang Dai, Xiaomeng Li   +3 more
semanticscholar   +1 more source

Solving large-scale support vector ordinal regression with asynchronous parallel coordinate descent algorithms

Pattern Recognition, 2021
Ordinal regression is one of the most influential tasks of supervised learning. Support vector ordinal regression (SVOR) is an appealing method to tackle ordinal regression problems.
Bin Gu, Xiang Geng, Wanli Shi, Yingying Shan, Yufang Huang, Zhijie Wang, Guansheng Zheng   +6 more
semanticscholar   +1 more source

Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation

Neural Information Processing Systems, 2023
Contrastive learning methods can be applied to deep regression by enforcing label distance relationships in feature space. However, these methods are limited to labeled data only unlike for classification, where unlabeled data can be used for contrastive
Weihang Dai, Yao Du, Hanru Bai, Kwang-Ting Cheng, Xiaomeng Li   +4 more
semanticscholar   +1 more source

Using ordinal regression for advancing the understanding of distinct suicide outcomes.

Suicide and Life-Threatening Behaviour, 2021
OBJECTIVE As recent advances in suicide research have underscored the importance of studying distinct suicide outcomes (i.e., suicidal thinking vs. behavior), there is a need to consider the theoretical meaningfulness of our statistical approach(es).
R. Jacobucci, Brooke A. Ammerman, Xiao-Bei Li   +2 more
semanticscholar   +1 more source

Ordinal Regression with Multiple Output CNN for Age Estimation

Computer Vision and Pattern Recognition, 2016
To address the non-stationary property of aging patterns, age estimation can be cast as an ordinal regression problem. However, the processes of extracting features and learning a regression model are often separated and optimized independently in ...
Zhenxing Niu, Mo Zhou, Le Wang, Xin-Bo Gao, Gang Hua   +4 more
semanticscholar   +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
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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
openaire   +1 more source