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
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 of real valued data
The Journal of Machine Learning Research
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
A regularization framework for multiple-instance learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Adaptive p-posterior mixture-model kernels for multiple instance learning
Proceedings of the 25th international conference on Machine learning
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
MILC2: a multi-layer multi-instance learning approach to video concept detection
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
A phase transition-based perspective on multiple instance kernels
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
LSA based multi-instance learning algorithm for image retrieval
Signal Processing
Bag dissimilarities for multiple instance learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Multi-instance multi-label learning
Artificial Intelligence
Latent topic based multi-instance learning method for localized content-based image retrieval
Computers & Mathematics with Applications
MI2LS: multi-instance learning from multiple informationsources
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2013 International Symposium on Wearable Computers
Multiple instance learning based on positive instance selection and bag structure construction
Pattern Recognition Letters
Hi-index | 0.00 |
Support vector machines (SVM) have been highly successful in many machine learning problems. Recently, it is also used for multi-instance (MI) learning by employing a kernel that is defined directly on the bags. As only the bags (but not the instances) have known labels, this MI kernel implicitly assumes all instances in the bag to be equally important. However, a fundamental property of MI learning is that not all instances in a positive bag necessarily belong to the positive class, and thus different instances in the same bag should have different contributions to the kernel. In this paper, we address this instance label ambiguity by using the method of marginalized kernels. It first assumes that all the instance labels are available and defines a label-dependent kernel on the instances. By integrating out the unknown instance labels, a marginalized kernel defined on the bags can then be obtained. A desirable property is that this kernel weights the instance pairs by the consistencies of their probabilistic instance labels. Experiments on both classification and regression data sets show that this marginalized MI kernel, when used in a standard SVM, performs consistently better than the original MI kernel. It also outperforms a number of traditional MI learning methods.