Multi-Instance Mixture Models

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Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vector. Under standard assumptions, MI learning can be understood as a type of semisupervised learning (SSL). The difference between MI learning and SSL is that positive bag labels provide weak label information for the instances that they contain. MI learning tasks can be approximated as SSL tasks by disregarding this weak label information, allowing the direct application of existing SSL techniques. To give insight into this connection we first introduce multi-instance mixture models (MIMMs), an adaption of mixture model classifiers for multi-instance data. We show how to learn such models using an Expectation-Maximization algorithm in the case where the instance-level class distributions are members of an exponential family. The cost of the semi-supervised approximation to multiinstance learning is explored, ...
James R. Foulds, Padhraic Smyth
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SDM
Authors James R. Foulds, Padhraic Smyth
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