OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth 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
Learning a kernel function for classification with small training samples
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data
Proceedings of the 24th international conference on Machine learning
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Convex Method for Locating Regions of Interest with Multi-instance Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multiple instance boosting for face recognition in videos
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Multiple-instance learning as a classifier combining problem
Pattern Recognition
One-Class multiple instance learning via robust PCA for common object discovery
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Horror video scene recognition based on multi-view multi-instance learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Extracting meronyms for a biology knowledge base using distant supervision
Proceedings of the 2013 workshop on Automated knowledge base construction
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
Robust subspace discovery via relaxed rank minimization
Neural Computation
Multiple instance learning based on positive instance selection and bag structure construction
Pattern Recognition Letters
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In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mechanism of how the instances determine the bag-labels is different for different application domains, and does not necessarily obey the traditional assumptions of MIL. We therefore propose an adaptive framework for MIL that adapts to different application domains by learning the domain-specific mechanisms merely from labeled bags. Our approach is especially attractive when we are encountered with novel application domains, for which the mechanisms may be different and unknown. Specifically, we exploit mixture models to represent the composition of each bag and an adaptable kernel function to represent the relationship between the bags. We validate on synthetic MIL datasets that the kernel function automatically adapts to different mechanisms of how the instances determine the bag-labels. We also compare our approach with state-of-the-art MIL techniques on real-world benchmark datasets.