Uncertainty in Artificial Intelligence
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Instance Label Prediction by Dirichlet Process Multiple Instance Learning
Melih Kandemir, Fred Hamprecht
We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simulta- neously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple in- stance constraints. We employ Dirichlet process priors on mixture weights to automate model se- lection, and efficiently infer model parameters and positive bag instances by a constrained varia- tional Bayes procedure. Our method improves on the state-of-the-art of instance classification from weak supervision on 20 benchmark text catego- rization data sets and one histopathology cancer diagnosis data set.
Pages: 380-389
PS Link:
PDF Link: /papers/14/p380-kandemir.pdf
AUTHOR = "Melih Kandemir and Fred Hamprecht",
TITLE = "Instance Label Prediction by Dirichlet Process Multiple Instance Learning",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "380--389"

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