Results 31 to 40 of about 5,941,658 (334)
Robust Image Ordinal Regression with Controllable Image Generation [PDF]
International Joint Conference on Artificial Intelligence, 2023 Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked.
Yi Cheng +7 more
semanticscholar +1 more source
Ordinal Regression Model of Parking Search Time
Promet (Zagreb), 2023 Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic.
Jelena Simićević +2 more
doaj +1 more source
Jambura Journal of Mathematics, 2023 Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people.
Assyifa Lala Pratiwi Hamid +4 more
doaj +1 more source
Deep Ordinal Regression Framework for No-Reference Image Quality Assessment
IEEE Signal Processing Letters, 2023 Due to the rapid development of deep learning techniques, no-reference image quality assessment (NR-IQA) has achieved significant improvement. NR-IQA aims to predict a real-valued variable for image quality, using the image in question as the sole input.
Hua-Sheng Wang +4 more
semanticscholar +1 more source
Active Learning for Imbalanced Ordinal Regression
IEEE Access, 2020 Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced ...
Jiaming Ge +4 more
doaj +1 more source
Pairwise Fairness for Ordinal Regression
CoRR, 2021 We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor has the form of a threshold model, composed of a scoring function and a set of thresholds, and our strategy is ...
Kleindessner, M. +4 more
openaire +5 more sources
Depth Estimation From Monocular Images And Sparse Radar Using Deep Ordinal Regression Network [PDF]
International Conference on Information Photonics, 2021 We integrate sparse radar data into a monocular depth estimation model and introduce a novel preprocessing method for reducing the sparseness and limited field of view provided by radar.
Chen-Chou Lo, P. Vandewalle
semanticscholar +1 more source
Ordinal regression: A review and a taxonomy of models
WIREs Computational Statistics, 2021 Ordinal models can be seen as being composed from simpler, in particular binary models. This view on ordinal models allows to derive a taxonomy of models that includes basic ordinal regression models, models with more complex parameterizations, the class
G. Tutz
semanticscholar +1 more source
Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels [PDF]
IEEE/CAA Journal of Automatica Sinica, 2022 The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss.
Yi-Ming Lei +3 more
semanticscholar +1 more source
Rank consistent ordinal regression for neural networks with application to age estimation [PDF]
Pattern Recognition Letters, 2019 In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
Wenzhi Cao, Vahid Mirjalili, S. Raschka
semanticscholar +1 more source