Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Active learning for logistic regression: an evaluation
Machine Learning
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Inconsistency-based active learning for support vector machines
Pattern Recognition
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The goal of pool-based active learning is to choose the best input points to gather output values from a `pool' of input samples. We develop two pool-based active learning criteria for linear regression. The first criterion allows us to obtain a closed-form solution so it is computationally very efficient. However, this solution is not necessarily optimal in the single-trial generalization error analysis. The second criterion can give a better solution, but it does not have a closed-form solution and therefore some additional search strategy is needed. To cope with this problem, we propose a practical procedure which enables us to efficiently search for a better solution around the optimal solution of the first method. Simulations with toy and benchmark datasets show that the proposed active learning method compares favorably with other active learning methods as well as the baseline passive learning scheme. Furthermore, the usefulness of the proposed active learning method is also demonstrated in wafer alignment in semiconductor exposure apparatus.