Learning Classification With Auxiliary Probabilistic Information (Quang Nguyen: Sep 23)

Finding ways of incorporating auxiliary information or auxiliary data into the learning process has been the topic of active data mining and machine learning research in recent years. In this work they studied and developed a new framework for classification learning problem in which, in addition to class labels, the learner is provided with an auxiliary (probabilistic) information that reflects how strong the expert feels about the class label. This approach can be extremely useful for many practical classification tasks that rely on subjective label assessment and where the cost of acquiring additional auxiliary information is negligible when compared to the cost of the example analysis and labeling. They developed classification algorithms capable of using the auxiliary information to make the learning process more efficient in terms of the sample complexity. He demonstrated the benefit of the approach on a number of synthetic and real world data sets by comparing it to the learning with class labels only.