Results 11 to 20 of about 5,941,658 (334)
Distributed Support Vector Ordinal Regression over Networks [PDF]
Entropy, 2022 Ordinal regression methods are widely used to predict the ordered labels of data, among which support vector ordinal regression (SVOR) methods are popular because of their good generalization.
Huan Liu, Jiankai Tu, Chunguang Li
doaj +2 more sources
Regularized Ordinal Regression and the ordinalNet R Package [PDF]
Journal of Statistical Software, 2021 Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection.
Michael J. Wurm +2 more
doaj +2 more sources
Unimodal Distributions for Ordinal Regression [PDF]
IEEE Transactions on Artificial Intelligence, 2023 In many real-world prediction tasks, the class labels contain information about the relative order between the labels that are not captured by commonly used loss functions such as multicategory cross-entropy.
J. S. Cardoso +2 more
semanticscholar +3 more sources
Deep Ordinal Regression Network for Monocular Depth Estimation. [PDF]
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 2018 Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional ...
Fu H +4 more
europepmc +3 more sources
Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks. [PDF]
BMC Bioinformatics, 2021 Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc.
Xu W, Gao Y, Wang Y, Guan J.
europepmc +2 more sources
Ordinal regression increases statistical power to predict epilepsy surgical outcomes [PDF]
Epilepsia Open, 2022 Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing ...
Adam S. Dickey +2 more
doaj +2 more sources
Support Vector Ordinal Regression [PDF]
Neural Computation, 2007 In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution.
Wei Chu, S. Sathiya Keerthi
openaire +4 more sources
Twitter Sentiment Analysis Based on Ordinal Regression
IEEE Access, 2019 In recent years, research on Twitter sentiment analysis, which analyzes Twitter data (tweets) to extract user sentiments about a topic, has grown rapidly. Many researchers prefer the use of machine learning algorithms for such analysis.
Shihab Elbagir Saad, Jing Yang
doaj +2 more sources
Predicting progression of Alzheimer's disease using ordinal regression. [PDF]
PLoS ONE, 2014 We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD ...
Orla M Doyle +10 more
doaj +2 more sources
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
openaire +2 more sources