Interactively training pixel classifiers

摘要

Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. To this end, it would be beneficial to produce training instancesinteractively. Rather than provide a supervised learning algorithm with one complete set of training examples before learning commences, it would be better to produce each new training instance based on knowledge of which instances the learner wouldotherwise misclassify. Whenever the learner receives one or more new training examples, it should update its classifier incrementally and, in real time, provide the teacher with feedback about its current performance. The feasibility of such an approachis demonstrated on a realistic image pixel classification task. Here, the number of training instances involved in building a classifier was reduced by several orders of magnitude, at no perceivable loss of classification accuracy.