document: Practical learning from one-sided feedback In many data mining applications , online labeling feedback is only available for examples which were predicted to belong to the positive class . Such applications includespam filtering in the case where users never checkemails marked `` spam '' , document retrieval where users cannotgive relevance feedback on unretrieved documents , and online advertising where user behavior can not beobserved for unshown advertisements . One-sided feedback can cripple the performance of classical mistake-driven online learners such as Perceptron . Previous work under the Apple Tasting framework showed how to transform standard online learners into successful learners from one sided feedback . However , we find in practice that this transformation may request more labels than necessary to achieve strong performance . In this paper , we employ two active learning methods which reduce the number of labels requested in practice . One method is the use of Label Efficient active learning . The other method , somewhat surprisingly , is the use of margin-based learners without modification , which we show combines implicit active learning and a greedy strategy to managing the exploration exploitation tradeoff . Experimental results show that these methods can be significantly more effective in practice than those using the Apple Tasting transformation , even on minority class problems .
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