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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.


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.


Sample moments, Minimax probability machine, Second order cone programming, Maize seeds classification.

Cite This Article

Yang, L., Gao, Y., Sun, Q. (2015). A New Minimax Probabilistic Approach and Its Application in Recognition the Purity of Hybrid Seeds. CMES-Computer Modeling in Engineering & Sciences, 104(6), 493–506.

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.
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