Smooth relevance vector machine: a smoothness prior extension of the RVM

@article{Schmolck2007SmoothRV,
  title={Smooth relevance vector machine: a smoothness prior extension of the RVM},
  author={Alexander Schmolck and Richard M. Everson},
  journal={Machine Learning},
  year={2007},
  volume={68},
  pages={107-135}
}
Enforcing sparsity constraints has been shown to be an effective and efficient way to obtain state-of-the-art results in regression and classification tasks. Unlike the support vector machine (SVM) the relevance vector machine (RVM) explicitly encodes the criterion of model sparsity as a prior over the model weights. However the lack of an explicit prior structure over the weight variances means that the degree of sparsity is to a large extent controlled by the choice of kernel (and kernel… CONTINUE READING
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