@inproceedings{b75d9488e931482099cfdbe0548a4b56,
title = "The bounds on the risk for sets of unbounded nonnegative functions on possibility space",
abstract = "Statistical learning theory on probability space is an important part of Machine Learning. Based on the key theorem, the bounds of uniform convergence have significant meaning. These bounds determine generalization ability of the learning machines utilizing the empirical risk minimization induction principle. In this paper, the bounds on the risk for sets of unbounded nonnegative functions on possibility space are discussed, and the rate of uniform convergence is estimated.",
keywords = "Credibility measure, Possibility space, The bounds on the risk for unbounded nonnegative functions, The empirical risk, The expected risk",
author = "Peng Wang and Zhang, \{Chun Qin\}",
year = "2011",
doi = "10.1109/ICMLC.2011.6016825",
language = "English",
isbn = "9781457703065",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "881--886",
booktitle = "Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011",
address = "United States",
note = "10th International Conference on Machine Learning and Cybernetics, ICMLC 2011 ; Conference date: 10-07-2011 Through 13-07-2011",
}