COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Convergence and Application of Online Active Sampling Using Orthogonal Pillar Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
ECG data compression using truncated singular value decomposition
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Block-Based Neural Networks for Personalized ECG Signal Classification
IEEE Transactions on Neural Networks
Remote sensing image segmentation by active queries
Pattern Recognition
ECG classification using ICA features and support vector machines
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Data structure-guided development of electrocardiographic signal characterization and classification
Artificial Intelligence in Medicine
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In this paper, we present three active learning strategies for the classification of electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these learning strategies select additional beat samples from a large set of unlabeled data. These samples are labeled manually, and then added to the training set. The entire procedure is iterated until the construction of a final training set representative of the considered classification problem. The proposedmethods are based on support vector machine classification and on the: 1) margin sampling; 2) posterior probability; and 3) query by committee principles, respectively. To illustrate their performance, we conducted an experimental study based on both simulated data and real ECG signals from the MIT-BIH arrhythmia database. In general, the obtained results show that the proposed strategies exhibit a promising capability to select samples that are significant for the classification process, i.e., to boost the accuracy of the classification process while minimizing the number of involved labeled samples.