A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
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
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Ordinal regression with sparse Bayesian
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
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This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.