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
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems
Pattern Recognition Letters
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
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
Localized content based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
On Learning Asymmetric Dissimilarity Measures
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
A new Hausdorff distance for image matching
Pattern Recognition Letters
Dissimilarity-based multiple instance learning
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Bag dissimilarities for multiple instance learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Reducing dimensionality in multiple instance learning with a filter method
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
On the informativeness of asymmetric dissimilarities
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the same manner. One of the ways to classify bags in MIL problems is through the use of bag dissimilarity measures. In current dissimilarity approaches, such dissimilarity measures act on the bag as a whole and do not distinguish between positive and negative bags. In this paper we explore whether this is a reasonable approach and when and why a dissimilarity measure that is dependent on the bag label, might be more appropriate.