Results 251 to 260 of about 90,248 (307)
Bayesian Mixed Models Approach to Exploring Resilience: Impact of Stress on Subjective Health and Affects Over Time During the COVID-19 Pandemic. [PDF]
Int J Methods Psychiatr ResSchepers M +10 more
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Relationship of Smile Esthetics and Quality of Life Among High-School Adolescents in Al-Ahsa, Saudi Arabia: An Analytic Cross-Sectional Study. [PDF]
Dent J (Basel)Alshaghdali M +3 more
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Application of the Ordinal Logistic Regression Models in Quality of life Studies
, 2014 Z Asadollahi +2 more
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Multiple-Instance Ordinal Regression
IEEE Transactions on Neural Networks and Learning Systems, 2018Ordinal 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, Zhifeng Hao
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2010
Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
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Within disaggregation–aggregation approach, ordinal regression aims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM).
Greco, Salvatore +3 more
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Collaborative ordinal regression
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006Ordinal 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.
Shipeng Yu +3 more
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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
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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
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Nonparallel Support Vector Ordinal Regression
IEEE Transactions on Cybernetics, 2017Ordinal regression is a supervised learning problem where training samples are labeled by an ordinal scale. The ordering relation and nonmetric property of the label set distinguish it from the multiclass classification and metric regression. To better exploit the inherent structure in the label and benefit from the hidden information in data ...
Huadong Wang +3 more
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