Hyper-sausage coverage function neuron model and learning algorithm for image classification

Xin Ning, Weijuan Tian, Feng He, Xiao Bai, Le Sun, Weijun Li

Abstract: Highlights•We propose a flexible HSCF neuron model, which adaptively changes the positions and directions of the one-dimensional simplex, as well as the radius of the hyperspheres. Thus, higher variability was assured for constructing the geometries, which helps to mine the potential data distribution.•A novel CE_VC loss function was proposed by constructing a volume-coverage loss function, which compresses the volume of the hyper-sausage to the hit, and thus the intra-class compactness of samples is assured.•We introduce a network learning algorithm that primarily conducts a divisive iteration method to determine the optimal hyperparameters adaptively.•Experiments using several datasets demonstrate the effectiveness and generalization ability of the proposed HSCF neuron in achieving excellent performance, including classification accuracy, complexity and computation.