Uncertainty in Artificial Intelligence
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Learning about individuals from group statistics
Hendrik Kuck, Nando de Freitas
Abstract:
We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an instance level classifier from this information. That is, we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics. We propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels. This model is trained with an efficient MCMC algorithm. Its performance is demonstrated on both synthetic and real-world data arising in general object recognition.
Keywords:
Pages: 332-339
PS Link:
PDF Link: /papers/05/p332-kuck.pdf
BibTex:
@INPROCEEDINGS{Kuck05,
AUTHOR = "Hendrik Kuck and Nando de Freitas",
TITLE = "Learning about individuals from group statistics",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "332--339"
}


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