Abstract: Fuzzy hypersphere support vector machine (FHS-SVM) has stronger generalization capability than hyperplane support vector machine in the one-class classification problem, being successful in radar target detection. Some hyperparameters have to be predefined before the FHS-SVM training, with different hyperparameters leading to significant difference in the FHS-SVM performance. In this paper, equivalence between FHS-SVM training and the level 1 Bayesian inference of the evidence framework is proved. Then, an FHS-SVM hyperparameter optimization iteration method is proposed based on the evidence framework. Using landmine detection data obtained with ultra-wide band synthetic aperture radar, the proposed iteration method is verified by comparing it with an exhaustive search method.

Key words: evidence framework, fuzzy hypersphere support vector machine(FHS-SVM), hyperparameter optimization, landmine detection