I have trained an SVM Regression model using training data, $x_1,x_2,\dots,x_N$.
I want to perform active learning to improve the model; i.e., I want to add more samples to the training data and relearn a better model, and to choose these new samples in such a way as to maximize the resulting model performance.
For an SVM classifier, a useful heuristic for active learning is to choose samples that fall close to the decision boundary; i.e., for a particular sample, the 'confidence' (c) can be computed using the SVM. Samples that have small |c| are more likely to improve the decision boundary in the retrained SVM.
Any suggestions on how to do this for SVM regression?
(I can generate samples at will, but it is costly to label them, so I want to know if I can use the 'already-trained' regression-SVM to help me decide which ones to label)