Instance Label Prediction by Dirichlet Process Multiple Instance Learning
Abstract
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. 1
Cite
Text
Kandemir and Hamprecht. "Instance Label Prediction by Dirichlet Process Multiple Instance Learning." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Kandemir and Hamprecht. "Instance Label Prediction by Dirichlet Process Multiple Instance Learning." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/kandemir2014uai-instance/)BibTeX
@inproceedings{kandemir2014uai-instance,
title = {{Instance Label Prediction by Dirichlet Process Multiple Instance Learning}},
author = {Kandemir, Melih and Hamprecht, Fred A.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {380-389},
url = {https://mlanthology.org/uai/2014/kandemir2014uai-instance/}
}