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Class Proportion Estimation with Application to Multiclass Anomaly Rejection
[article]
2014
arXiv
pre-print
This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class. Compared to previous work on this problem, our approach has the novel feature that it does not require labeled training data from one of the
arXiv:1306.5056v3
fatcat:hou5hm7sljhgvbkjbbftiw4mb4