Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem ...
This paper addresses the training of classification trees for weakly labelled data. We call ”weakly labelled data”, a training set such as the prior labelling information pro...
Decision tree-based probability estimation has received great attention because accurate probability estimation can possibly improve classification accuracy and probability-based r...
AUC(Area Under the Curve) of ROC(Receiver Operating Characteristics) has been recently used as a measure for ranking performanceof learning algorithms. In this paper, wepresent a ...
We present an algorithm, called the offset tree, for learning in situations where a loss associated with different decisions is not known, but was randomly probed. The algorithm i...