The nature of statistical learning theory
The nature of statistical learning theory
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Neural Computation
Learning from ambiguity
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Journal of Machine Learning Research
Adaptive p-posterior mixture-model kernels for multiple instance learning
Proceedings of the 25th international conference on Machine learning
On profiling blogs with representative entries
Proceedings of the second workshop on Analytics for noisy unstructured text data
Multiple-Instance Active Learning for Image Categorization
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Region-based automatic web image selection
Proceedings of the international conference on Multimedia information retrieval
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
Multiple instance boosting for face recognition in videos
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
"Tell me more": how semantic technologies can help refining internet image search
Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications
Multiple instance classification: Review, taxonomy and comparative study
Artificial Intelligence
A feature-word-topic model for image annotation and retrieval
ACM Transactions on the Web (TWEB)
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We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other SVM-based MIL methods, our approach more directly enforces the desired constraint that at least one of the instances in a positive bag is positive. Using both artificial and real-world data, we experimentally demonstrate that our approach achieves greater accuracy than state-of-the-art MIL methods when positive bags are sparse, and performs competitively when they are not. In particular, our approach is the best performing method for image region classification.