Results 281 to 290 of about 5,941,658 (334)
Regression Models for Ordinal Data
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1980 Summary A general class of regression models for ordinal data is developed and discussed. These models utilize the ordinal nature of the data by describing various modes of stochastic ordering and this eliminates the need for assigning scores or otherwise assuming cardinality instead of ordinality.
P. McCullagh
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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
Peter Tino
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Penalized Regression with Ordinal Predictors [PDF]
International Statistical Review, 2009 SummaryOrdered categorial predictors are a common case in regression modelling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this paper, existing methods are reviewed and the use of penalized regression techniques is proposed.
Gerhard Tutz
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IEEE journal of biomedical and health informatics, 2022
Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis.
Tan N. N. Doan +4 more
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Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis.
Tan N. N. Doan +4 more
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Convolutional Ordinal Regression Forest for Image Ordinal Estimation [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different ...
Haiping Zhu, Fei-Yue Wang, Hongming Shan
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CORNet: Context-Based Ordinal Regression Network for Monocular Depth Estimation
IEEE transactions on circuits and systems for video technology (Print), 2022Monocular depth estimation, as one of the fundamental tasks of computer vision, plays a crucial role in three-dimensional (3D) scene understanding and perception.
Xuyang Meng +3 more
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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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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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Incremental learning algorithm for large-scale semi-supervised ordinal regression
Neural Networks, 2022As a special case of multi-classification, ordinal regression (also known as ordinal classification) is a popular method to tackle the multi-class problems with samples marked by a set of ranks.
Hai-Yan Chen +3 more
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A Survey on Ordinal Regression: Applications, Advances and Prospects
arXiv.orgOrdinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image aesthetics assessment, and even cancer staging, due to its capability to ...
Jin-Hong Wang +5 more
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