Real-Valued Multiple-Instance Learning with Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Multiple instance learning of real valued data
The Journal of Machine Learning Research
Journal on Image and Video Processing
Journal on Image and Video Processing
Real-valued multiple-instance learning with queries
Journal of Computer and System Sciences
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There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.