@inproceedings{llorens2011pac-bayesian,
author = {Llorens, Ashley J. and Wang, I-Jeng},
title = {PAC-Bayesian learning with asymmetric cost},
booktitle = {2011 IEEE Signal Processing Workshop on Statistical Signal Processing},
year = {2011},
month = {June},
abstract = {PAC-Bayes generalization bounds offer a theoretical foundation for learning classifiers with low generalization error and predicting their performance on unseen data. Current formulations implicitly assume that the relative cost of misclassifying a positive or negative example is reflected by the class skew in the training dataset. We present a learning approach based on minimizing an asymmetric generalization bound that enables PAC-Bayesian model selection under a class-specific performance constraint.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/pac-bayesian-learning-with-asymmetric-cost/},
pages = {765-768},
}