An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.

中文翻译：

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基于核整体学习和划分的神经网络优化分类方法

提出了一种基于核整体学习与除法的优化神经网络分类方法。该方法以学习的径向基函数核为研究对象。此处提出的内核可被视为由训练样本空间中的相同模式类别组成的子空间区域。通过扩展原始实例的样本空间的区域，可以从子空间获得实例之间的相关信息，并且分类器的边界可以远离原始实例。因此，增强了分类器的鲁棒性和泛化性能。在具体实现中，根据实例优化和筛选方法在每个RBF内核中生成一个新的模式向量，以表征KHLD。