Learning with labeled sessions

Traditional supervised learning deals with labeled instances. In many applications such as physiological data modeling and speaker identification, however, training examples are often labeled objects and each of the labeled objects consists of multiple unlabeled instances. When classifying a new object, its class is determined by the majority of its instance classes. As a consequence of this decision rule, one challenge to learning with labeled objects (or sessions) is to determine during training which subset of the instances inside an object should belong to the class of the object. We call this type of learning 'session-based learning' to distinguish it from the traditional supervised learning. In this paper, we introduce session-based learning problems, give a formal description of session-based learning in the context of related work, and propose an approach that is particularly designed for session-based learning. Empirical studies with UCI datasets and real-world data show that the proposed approach is effective for session-based learning.