Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Active relevance feedback for difficult queries
Proceedings of the 17th ACM conference on Information and knowledge management
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For many types of learners one can compute the statistically `optimal'' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.