Empirical survival error potential weighted least squares for binary pattern classification

Lei Sun; Kar Ann Toh; Zhiping Lin; Badong Chen

doi:10.1109/ICARCV.2014.7064433

Empirical survival error potential weighted least squares for binary pattern classification

Lei Sun, Kar Ann Toh, Zhiping Lin, Badong Chen

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	949-952
Number of pages	4
ISBN (Electronic)	9781479951994
DOIs	https://doi.org/10.1109/ICARCV.2014.7064433
Publication status	Published - 2014
Event	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore Duration: 2014 Dec 10 → 2014 Dec 12

Publication series

Name	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Other

Other	2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
Country/Territory	Singapore
City	Singapore
Period	14/12/10 → 14/12/12

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Human-Computer Interaction
Artificial Intelligence
Control and Systems Engineering

Access to Document

10.1109/ICARCV.2014.7064433

Cite this

Sun, L., Toh, K. A., Lin, Z., & Chen, B. (2014). Empirical survival error potential weighted least squares for binary pattern classification. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 (pp. 949-952). [7064433] (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV.2014.7064433

Sun, Lei ; Toh, Kar Ann ; Lin, Zhiping et al. / Empirical survival error potential weighted least squares for binary pattern classification. 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 949-952 (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014).

@inproceedings{608eb4e8ddef48579737591c61438056,

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.",

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",

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}

Sun, L, Toh, KA, Lin, Z & Chen, B 2014, Empirical survival error potential weighted least squares for binary pattern classification. in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014., 7064433, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Institute of Electrical and Electronics Engineers Inc., pp. 949-952, 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, Singapore, Singapore, 14/12/10. https://doi.org/10.1109/ICARCV.2014.7064433

Empirical survival error potential weighted least squares for binary pattern classification. / Sun, Lei; Toh, Kar Ann; Lin, Zhiping et al.
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 949-952 7064433 (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Empirical survival error potential weighted least squares for binary pattern classification

AU - Sun, Lei

AU - Toh, Kar Ann

AU - Lin, Zhiping

AU - Chen, Badong

PY - 2014

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N2 - 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.

AB - 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.

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BT - 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

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ER -

Sun L, Toh KA, Lin Z, Chen B. Empirical survival error potential weighted least squares for binary pattern classification. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 949-952. 7064433. (2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014). doi: 10.1109/ICARCV.2014.7064433

Empirical survival error potential weighted least squares for binary pattern classification

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