Making large-scale support vector machine learning practical
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Gradient LASSO for feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
The evidence framework applied to classification networks
Neural Computation
Bayesian support vector regression using a unified loss function
IEEE Transactions on Neural Networks
Feature Selection for MLP Neural Network: The Use of Random Permutation of Probabilistic Outputs
IEEE Transactions on Neural Networks
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This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiment shows that the proposed method generally performs better, and at least as well as the existing methods, with notable advantage when the data set is sparse.