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 D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Multiple-Instance Learning of Real-Valued Data
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ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
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Neural Computation
Multiple instance learning of real valued data
The Journal of Machine Learning Research
SVM-based generalized multiple-instance learning via approximate box counting
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Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Supervised versus multiple instance learning: an empirical comparison
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Support vector machines for histogram-based image classification
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On the relation between multi-instance learning and semi-supervised learning
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Cuts3vm: a fast semi-supervised svm algorithm
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Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
A Convex Method for Locating Regions of Interest with Multi-instance Learning
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Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A human-centered multiple instance learning framework for semantic video retrieval
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Linear time maximum margin clustering
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Localized content-based image retrieval using semi-supervised multiple instance learning
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MILC2: a multi-layer multi-instance learning approach to video concept detection
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The Knowledge Engineering Review
Risk-distortion analysis for video collusion attacks: a mouse-and-cat game
IEEE Transactions on Image Processing
Building sparse support vector machines for multi-instance classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Multi-instance multi-label learning
Artificial Intelligence
A Simple and Fast Multi-instance Classification via Support Vector Machine
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
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This paper focuses on kernel methods for multi-instance learning. Existing methods require the prediction of the bag to be identical to the maximum of those of its individual instances. However, this is too restrictive as only the sign is important in classification. In this paper, we provide a more complete regularization framework for MI learning by allowing the use of different loss functions between the outputs of a bag and its associated instances. This is especially important as we generalize this for multi-instance regression. Moreover, both bag and instance information can now be directly used in the optimization. Instead of using heuristics to solve the resultant non-linear optimization problem, we use the constrained concave-convex procedure which has well-studied convergence properties. Experiments on both classification and regression data sets show that the proposed method leads to improved performance.