Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Support Vector Ordinal Regression
Neural Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
On Relevant Dimensions in Kernel Feature Spaces
The Journal of Machine Learning Research
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Sparse least squares support vector regressors trained in the reduced empirical feature space
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
An experimental study of different ordinal regression methods and measures
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
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The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle nonlinear decision regions has been proven by the use of a non-linearly separable toy dataset.