Empirical survival error potential weighted least squares for binary pattern classification

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages949-952
Number of pages4
ISBN (Electronic)9781479951994
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: 2014 Dec 102014 Dec 12

Publication series

Name2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period14/12/1014/12/12

Fingerprint

Pattern recognition
Error compensation
Compensation and Redress

All Science Journal Classification (ASJC) codes

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

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 ; Chen, Badong. / 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).
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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.",
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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; Chen, Badong.

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 proceedingConference contribution

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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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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). https://doi.org/10.1109/ICARCV.2014.7064433