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
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth 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
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
An Extended Kernel for Generalized Multiple-Instance Learning
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
Locating regions of interest in CBIR with multi-instance learning techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Active concept learning in image databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
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A generalized discriminative multiple instance learning (GDMIL) algorithm is presented to train the classifier in the condition of vague annotation of training samples GDMIL not only inherits the original MIL's capability of automatically weighting the instances in the bag according to their relevance to the concept but also integrates generative models using discriminative training. It is evaluated on the task of multimedia semantic concept detection using the development data set of TRECVID 2005. The experimental results show GDMIL outperforms the baseline systems trained on MIL with diverse density and expectation-maximization diverse density and the system without MIL.