Linguistics and face recognition
Journal of Visual Languages and Computing
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
Stream-based active unusual event detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Inconsistency-based active learning for support vector machines
Pattern Recognition
Robust re-identification using randomness and statistical learning: Quo vadis
Pattern Recognition Letters
| Hi-index | 0.14 |
There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting.