COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Active learning: theory and applications
Active learning: theory and applications
Robust Real-Time Face Detection
International Journal of Computer Vision
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
The random electrode selection ensemble for EEG signal classification
Pattern Recognition
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Active learning with statistical models
Journal of Artificial Intelligence Research
An algorithm on multi-view adaboost
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Hessian optimal design for image retrieval
Pattern Recognition
Inconsistency-based active learning for support vector machines
Pattern Recognition
Semi-supervised multitask learning via self-training and maximum entropy discrimination
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Information Sciences: an International Journal
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
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Generally, collecting a large quantity of unlabeled examples is feasible, but labeling them all is not. Active learning can reduce the number of labeled examples needed to train a good classifier. Existing active learning algorithms can be roughly divided into three categories: single-view single-learner (SVSL) active learning, multiple-view single-learner (MVSL) active learning and single-view multiple-learner (SVML) active learning. In this paper, a new approach that incorporates multiple views and multiple learners (MVML) into active learning is proposed. Multiple artificial neural networks are used as learners in each view, and they are set with different numbers of hidden neurons and weights to ensure each of them has a different bias. The selective sampling of our proposed method is implemented in three different ways. For comparative purpose, the traditional methods MVSL and SVML active learning as well as bagging active learning and adaboost active learning are also implemented together with MVML active learning in our experiments. The empirical results indicate that the MVML active learning outperforms the other traditional methods.