Active Sampling for Class Probability Estimation and Ranking.

In many cost-sensitive environments class probability estimates are used by decision makers to evaluate the expected utility from a set of alternatives. Supervised learning can be used to build class probability estimates; however, it often is very costly to obtain training data with class labels. A...

詳細記述

書誌詳細

出版年:	Machine learning. 54, 2 (2004).
第一著者:	Saar-Tsechansky, Maytal
フォーマット:	論文
言語:	English
主題:	Active learning. Cost-sensitive learning. Class probability estimation. Ranking. Supervised learning. Decision trees. Uncertainty sampling. Selective sampling.