A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Incremental Active Learning for Optimal Generalization
Neural Computation
The Journal of Machine Learning Research
Pool-based active learning in approximate linear regression
Machine Learning
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
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Pool-based batch active learning is aimed at choosing training inputs from a 'pool' of test inputs so that the generalization error is minimized. P-ALICE (Pool-based Active Learning using Importance-weighted least-squares learning based on Conditional Expectation of the generalization error) is a state-of-the-art method that can cope with model misspecification by weighting training samples according to the importance (i.e., the ratio of test and training input densities). However, importance estimation in the original P-ALICE is based on the assumption that the number of training samples to gather is small, which is not always true in practice. In this paper, we propose an alternative scheme for importance estimation based on the inclusion probability, and show its validity through numerical experiments.