Selective Sampling Using the Query by Committee Algorithm
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
The Journal of Machine Learning Research
SVM-based active feedback in image retrieval using clustering and unlabeled data
Pattern Recognition
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
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
Batch-Mode Active Learning with Semi-supervised Cluster Tree for Text Classification
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Active Learning with Bootstrapped Dendritic Classifier applied to medical image segmentation
Pattern Recognition Letters
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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In this paper, we propose an active learning technique for solving multiclass problems with support vector machine (SVM) classifiers. The technique is based on both uncertainty and diversity criteria. The uncertainty criterion is implemented by analyzing the one-dimensional output space of the SVM classifier. A simple histogram thresholding algorithm is used to find out the low density region in the SVM output space to identify the most uncertain samples. Then the diversity criterion exploits the kernel k-means clustering algorithm to select uncorrelated informative samples among the selected uncertain samples. To assess the effectiveness of the proposed method we compared it with other batch mode active learning techniques presented in the literature using one toy data set and three real data sets. Experimental results confirmed that the proposed technique provided a very good tradeoff among robustness to biased initial training samples, classification accuracy, computational complexity, and number of new labeled samples necessary to reach the convergence.