A practical Bayesian framework for backpropagation networks
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Accurate on-line support vector regression
Neural Computation
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In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order. Smoothness is achieved by applying a roughness penulty, a concept from the area of functional data analysis. Sparscness is taken care of by automatic relevance determination. Both are coinbined in u Bayesian inodel, which has been implemented and tested. Test results arc presented in the paper.