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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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Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012Ordinal 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 +3 more
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Group truncated ordinal regression
Statistics & Probability Letters, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
O'Neill, Terence J., Barry, Simon C.
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2001
Many medical and epidemiologic studies incorporate an ordinal response variable. In some cases an ordinal response Y represents levels of a standard measurement scale such as severity of pain (none, mild, moderate, severe). In other cases, ordinal responses are constructed by specifying a hierarchy of separate endpoints.
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Many medical and epidemiologic studies incorporate an ordinal response variable. In some cases an ordinal response Y represents levels of a standard measurement scale such as severity of pain (none, mild, moderate, severe). In other cases, ordinal responses are constructed by specifying a hierarchy of separate endpoints.
openaire +1 more source