Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Computation
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
A generic framework for event detection in various video domains
Proceedings of the international conference on Multimedia
Multi-graph multi-instance learning for object-based image and video retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Semi-supervised multi-instance multi-label learning for video annotation task
Proceedings of the 20th ACM international conference on Multimedia
Coupled attribute analysis on numerical data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Multiple instance learning (MIL) is a branch of machine learning that attempts to learn information from bags of instances. Many real-world applications such as localized content-based image retrieval and text categorization can be viewed as MIL problems. In this paper, we propose a new graph-based semi-supervised learning approach for multiple instance learning. By defining an instance-level graph on the data, we first propose a new approach to construct an optimization framework for multiple instance semi-supervised learning, and derive an efficient way to overcome the non-convexity of MIL. We empirically show that our method outperforms state-of-the-art MIL algorithms on several real-world data sets.