Incremental value of prostate-specific antigen density beyond PI-RADS and conventional MRI features for predicting the highest ISUP grade group in patients with prostate cancer diagnosed at initial systematic prostate biopsy. [PDF]
Am J Cancer ResFan D, Niu R, Zhou F, Yang L, Zhang C.
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Seasonal Nile Crocodile Damage to Fishing Gear, Incident Reporting and Livelihood Responses in the Sudd Wetlands, South Sudan. [PDF]
Animals (Basel)Benansio JS +3 more
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Response to the Letter to the Editor Regarding "Knowledge and Attitudes Toward Attention-Deficit/Hyperactivity Disorder Among Physicians and Senior Medical Students in Jordan" [Response To Letter]. [PDF]
Adv Med Educ PractAl-Ghawanmeh R +7 more
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Collaborative ordinal regression
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 Ordinal 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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Ordinal regression based on learning vector quantization [PDF]
Neural Networks, 2017 Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the
Tang Fengzhen, Peter Tino
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Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering, 2010 Ordinal regression has wide applications in many domains where the human evaluation plays a major role. Most current ordinal regression methods are based on Support Vector Machines (SVM) and suffer from the problems of ignoring the global information of ...
Desheng Dash Wu, Jiuyong Li
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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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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 0002, Zhifeng Hao
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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 0007 +3 more
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