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
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multiple kernel learning, conic duality, and the SMO algorithm
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
Multi-Instance Learning Based Web Mining
Applied Intelligence
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
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
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A de-texturing and spatially constrained K-means approach for image segmentation
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
LSA based multi-instance learning algorithm for image retrieval
Signal Processing
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
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Previous studies on multiple instance learning (MIL) have shown that the MIL problem holds three characteristics: positive instance clustering, bag structure and instance probabilistic influence to bag label. In this paper, combined with the advantages of these three characteristics, we propose two simple yet effective MIL algorithms, CK_MIL and ck_MIL. We take three steps to convert MIL to a standard supervised learning problem. In the first step, we perform K-means clustering algorithm on the positive and negative sets separately to obtain the cluster centers, further use them to select the most positive instances in bags. Next, we combine three distances, including the maximum, minimum and the average distances from bag to cluster centers, as bag structure. For CK_MIL, we simply compose the positive instance and bag structure to form a new vector as bag representation, then apply RBF kernel to measure bag similarity, while for ck_MIL algorithm we construct a new kernel by introducing a probabilistic coefficient to balance the influences between the positive instance similarity and bag structure similarity. As a result, the MIL problem is converted to a standard supervised learning problem that can be solved directly by SVM method. Experiments on MUSK and COREL image set have shown that our two algorithms perform better than other key existing MIL algorithms on the drug prediction and image classification tasks.