Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Convergence of a block coordinate descent method for nondifferentiable minimization
Journal of Optimization Theory and Applications
Order SVM: a kernel method for order learning based on generalized order statistics
Systems and Computers in Japan
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
The Journal of Machine Learning Research
The unimodal model for the classification of ordinal data
Neural Networks
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
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In this study, we propose a learning algorithm for ordinal regression problems. In most existing learning algorithms, the threshold or location model is assumed to be the statistical model. For estimation of conditional probability of labels for a given covariate vector, we extended the location model to apply ordinal regressions. We present this learning algorithm using the squared-loss function with the location-scale models for estimating conditional probability. We prove that the estimated conditional probability satisfies the monotonicity of the distribution function. Furthermore, we have conducted numerical experiments to compare these proposed methods with existing approaches. We found that, in its ability to predict labels, our method may not have an advantage over existing approaches. However, for estimating conditional probabilities, it does outperform the learning algorithm using location models.