@inproceedings{05926979c31c4118894453735ed5f4b2,
title = "Empirical survival error potential weighted least squares for binary pattern classification",
abstract = "A weighted least squares scheme based on an empirical survival error potential function is proposed in this paper. The empirical survival error potential function provides an error compensation scheme for noise distributions far from being Gaussian. This error compensation procedure is efficiently implemented via a weighted least squares formulation where an analytical solution form is obtained. The performance of the developed scheme is extensively tested on 16 benchmark data sets where the results show promising potential of the proposed empirical survival error distribution compensation scheme for binary pattern classification.",
keywords = "Binary Classification, Survival Information Potential, Weighted Least Squares",
author = "Lei Sun and Toh, \{Kar Ann\} and Zhiping Lin and Badong Chen",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 ; Conference date: 10-12-2014 Through 12-12-2014",
year = "2014",
doi = "10.1109/ICARCV.2014.7064433",
language = "English",
series = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "949--952",
booktitle = "2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014",
address = "United States",
}