Query by transduction
There has recently been 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. Toward 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 multiclass classification tasks using a support vector machine (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.
transductive inference active learning support vector machine hypothesis testing kolmogorov complexity