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Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
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Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Multi-objective Genetic Programming for Multiple Instance Learning
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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Expert Systems with Applications: An International Journal
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Applied Intelligence
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DS '09 Proceedings of the 12th International Conference on Discovery Science
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IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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Applied Soft Computing
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Information Sciences: an International Journal
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MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Artificial Intelligence
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
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HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
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In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. A bag is positive if it contains at least one positive instance, while it is negative if it contains no positive instance. In this paper, a neural network based multi-instance learning algorithm named RBF-MIP is presented, which is derived from the popular radial basis function (RBF) methods. Briefly, the first layer of an RBF-MIP neural network is composed of clusters of bags formed by merging training bags agglomeratively, where Hausdorff metric is utilized to measure distances between bags and between clusters. Weights of second layer of the RBF-MIP neural network are optimized by minimizing a sum-of-squares error function and worked out through singular value decomposition (SVD). Experiments on real-world multi-instance benchmark data, artificial multi-instance benchmark data and natural scene image database retrieval are carried out. The experimental results show that RBF-MIP is among the several best learning algorithms on multi-instance problems.