Results 251 to 260 of about 2,450,039 (300)

Modeling treatment effects on absorbing outcomes in clinical trials: Leveraging longitudinal and ordinal data for efficiency gains. [PDF]

Stat Methods Med Res
Di Gravio C, Tao R, Nam Y, Schmidt EP, Shapiro N, Schildcrout JS.   +5 more
europepmc   +1 more source

Online Food Purchase Frequency Across Generations: Associations with Healthy Eating, Environmental Concern, and Economic Decision Indicators. [PDF]

Nutrients
Wolny R, Awdziej M, Jaciow M, Lipowska I, Lipowski M, Tkaczyk J.   +5 more
europepmc   +1 more source

Ordinal sentiment classification in cancer support forums: a controlled benchmark of machine learning and transformer models. [PDF]

Front Digit Health
Wang Z, Cao Y, Xu S, Ding Z, Gao Y, Mao Q, Qi T, Wang H.   +7 more
europepmc   +1 more source

Ordinal Regression Models

, 2015
openaire   +1 more source

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, Kai Yu 0001, Volker Tresp, Hans-Peter Kriegel   +3 more
openaire   +2 more sources

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
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Robust Ordinal Regression

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, Słowiński, Roman, Figueira, José Rui, Mousseau, Vincent   +3 more
openaire   +1 more source

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