A practical Bayesian framework for backpropagation networks
Neural Computation
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Minimum Enclosing Spheres Formulations for Support Vector Ordinal Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Binary and graded relevance in IR evaluations-Comparison of the effects on ranking of IR systems
Information Processing and Management: an International Journal
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sparse bayesian kernel survival analysis for modeling the growth domain of microbial pathogens
IEEE Transactions on Neural Networks
Validation based sparse gaussian processes for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
A probabilistic least squares approach to ordinal regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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In this paper, a probabilistic framework for ordinal prediction is proposed, which can be used in modeling ordinal regression. A sparse Bayesian treatment for ordinal regression is given by us, in which an automatic relevance determination prior over weights is used. The inference techniques based on Laplace approximation is adopted for model selection. By this approach accurate prediction models can be derived, which typically utilize dramatically fewer basis functions than the comparable supported vector based and Gaussian process based approaches while offering a number of additional advantages. Experimental results on the real-world data set show that the generalization performance competitive with support vector-based method and Gaussian process-based method.