Adaptive and non-adaptive perceptron-like algorithms for selective sampling tasks
Foundations of Active Learning workshop at NIPS 2005
A selective sampling algorithm is a learning algorithm for classification
that, based on the past observed data, decides whether to sample the label of each new instance to be classified. In this paper we compare both theoretically and empirically two Perceptron-like selective sampling algorithms for binary classification.
The empirical comparison is carried out on two well-known medium-size
real-world datasets for OCR tasks. The outcome of these experiments (essentially predicted by the theoretical analysis) shows that our selective sampling algorithms tend to perform as good as the algorithms
receiving the true label after each classification, while observing in practice substantially fewer labels.