Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Organizing textual documents into a hierarchical taxonomy is a common practice in knowledge management. Beside textual features, the hierarchical structure of directories reflect...
Yi Huang, Kai Yu, Matthias Schubert, Shipeng Yu, V...
Mining frequent patterns is a major topic in data mining research, resulting in many seminal papers and algorithms on item set and episode discovery. The combination of these, call...
: We combine the speed and scalability of information retrieval with the generally superior classification accuracy offered by machine learning, yielding a two-phase text classifie...