Table of Content

Open Access iconOpen Access

ARTICLE

A New Minimax Probabilistic Approach and Its Application in Recognition the Purity of Hybrid Seeds

Liming Yang1, Yongping Gao2, Qun Sun3

College of Science,China Agricultural University, Beijing, 100083, China.
Capital Normal University, Beijing, 100048, China.
College of Agriculture and Biotechnology, China Agricultural University, Beijing, 100193, China.

Computer Modeling in Engineering & Sciences 2015, 104(6), 493-506. https://doi.org/10.3970/cmes.2015.104.493

Abstract

Minimax probability machine (MPM) has been recently proposed and shown its advantage in pattern recognition. In this paper, we present a new minimax probabilistic approach (MPA),which can provide an explicit lower bound on prediction accuracy. Applying the Chebyshev-Cantelli inequality, the MPA is posed as a second order cone program formulation and solved effectively. Following that, this method is exploited directly to recognize the purity of hybrid seeds using near-infrared spectroscopic data. Experimental results in different spectral regions show that the proposed MPA is competitive with the existing minimax probability machine and support vector machine in generalization, while requires less computational time than them. These results illustrate the feasibility and effectiveness of the proposed approach in recognition the purity of hybrid seeds.

Keywords


Cite This Article

APA Style
Yang, L., Gao, Y., Sun, Q. (2015). A new minimax probabilistic approach and its application in recognition the purity of hybrid seeds. Computer Modeling in Engineering & Sciences, 104(6), 493-506. https://doi.org/10.3970/cmes.2015.104.493
Vancouver Style
Yang L, Gao Y, Sun Q. A new minimax probabilistic approach and its application in recognition the purity of hybrid seeds. Comput Model Eng Sci. 2015;104(6):493-506 https://doi.org/10.3970/cmes.2015.104.493
IEEE Style
L. Yang, Y. Gao, and Q. Sun "A New Minimax Probabilistic Approach and Its Application in Recognition the Purity of Hybrid Seeds," Comput. Model. Eng. Sci., vol. 104, no. 6, pp. 493-506. 2015. https://doi.org/10.3970/cmes.2015.104.493



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1325

    View

  • 830

    Download

  • 0

    Like

Share Link