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

作者：Yang, Liming^*; Gao, Yongping; Sun, Qun

来源：Computer Modeling in Engineering & Sciences, 2015, 104(6): 493-506.

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

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.

• 出版日期2015-2
• 单位首都师范大学; 中国农业大学