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
On Bayesian classification with Laplace priors
Pattern Recognition Letters
Sparse Bayesian nonparametric regression
Proceedings of the 25th international conference on Machine learning
Probabilistic classification vector machines
IEEE Transactions on Neural Networks
Probabilistic classifiers with a generalized Gaussian scale mixture prior
Pattern Recognition
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Most of the existing probit classifiers are based on sparsity-oriented modeling. However, we show that sparsity is not always desirable in practice, and only an appropriate degree of sparsity is profitable. In this work, we propose a flexible probabilistic model using a generalized Gaussian scale mixture prior that can promote an appropriate degree of sparsity for its model parameters, and yield either sparse or non-sparse estimates according to the intrinsic sparsity of features in a dataset. Model learning is carried out by an efficient modified maximum a posteriori (MAP) estimate. We also show relationships of the proposed model to existing probit classifiers as well as iteratively re-weighted l1 and l2 minimizations. Experiments demonstrate that the proposed method has better or comparable performances in feature selection for linear classifiers as well as in kernel-based classification.