Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Approximating hyper-rectangles: learning and pseudo-random sets
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
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
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiple-Instance Learning of Real-Valued Geometric Patterns
Annals of Mathematics and Artificial Intelligence
Learning from ambiguity
Multiple-Instance Learning of Real-Valued Geometric Patterns
Annals of Mathematics and Artificial Intelligence
Improve Multi-Instance Neural Networks through Feature Selection
Neural Processing Letters
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Quantitative pharmacophore models with inductive logic programming
Machine Learning
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The multiple-instance model was motivated by the drug activity prediction problem where each example is a possible configuration for a molecule and each bag contains all likely configurations for the molecule. While there has been a significant amount of theoretical and empirical research directed towards this problem, most research performed under the multiple-instance model is for concept learning. However, binding affinity between molecules and receptors is quantitative and hence a real-valued classification is preferable.In this paper we initiate a theoretical study of real-valued multiple instance learning. We prove that the problem of finding a target point consistent with a set of labeled multiple-instance examples (or bags) is NP-complete. We also prove that the problem of learning from realvalued multiple-instance examples is as hard as learning DNF. Another contribution of our work is in defining and studying a multiple-instance membership query (MI-MQ). We give a positive result on exactly learning the target point for a multiple-instance problem in which the learner is provided with a MI-MQ oracle and a single adversarially selected bag.