• Corpus ID: 10399946

Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data

@article{Behmardi2016ConfidenceConstrainedME,
  title={Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data},
  author={Behrouz Behmardi and Forrest Briggs and Xiaoli Z. Fern and Raviv Raich},
  journal={ArXiv},
  year={2016},
  volume={abs/1603.01901}
}
Multi-instance data, in which each object (bag) contains a collection of instances, are widespread in machine learning, computer vision, bioinformatics, signal processing, and social sciences. We present a maximum entropy (ME) framework for learning from multi-instance data. In this approach each bag is represented as a distribution using the principle of ME. We introduce the concept of confidence-constrained ME (CME) to simultaneously learn the structure of distribution space and infer each… 

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