Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)
We consider the general problem of learning multi-category classifi(cid:173) cation from labeled examples. We present experimental results for a nearest neighbor algorithm which actively selects samples from different pattern classes according to a querying rule instead of the a priori class probabilities. The amount of improvement of this query-based approach over the passive batch approach depends on the complexity of the Bayes rule. The principle on which this al(cid:173) gorithm is based is general enough to be used in any learning algo(cid:173) rithm which permits a model-selection criterion and for which the error rate of the classifier is calculable in terms of the complexity of the model.