C4.5: programs for machine learning
C4.5: programs for machine learning
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiple instance learning with generalized support vector machines
Eighteenth national conference on Artificial intelligence
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
| Hi-index | 0.00 |
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.