HCFNN: High-order coverage function neural network for image classification

Highlights：

• HCFNN architecture based on the HCF neuron is proposed; it is used to mine specific feature representations and achieve adaptive parameter learning. Next, a novel adaptive optimization method for weights and hyper-parameters is proposed to achieve effective network learning. The 100 learned network model has better expression and learning ability with fewer neurons.

• We conduct experiments on nine datasets in several domains, including the two-spirals problem, natural object recognition, face recognition, and person re-ID. Experimental results show that the proposed method has better 105 learning performance and generalization ability than the commonly used M-P and RBF neural networks. In addition, our method can improve the performance of various image recognition tasks and acquire good generalization.

摘要

•A more flexible HCF neuron model for DNNs is introduced; it constructs geometries in an n-dimensional space by changing weights and hyper-parameters and thus, possesses higher variability and plasticity. Furthermore, the approximation theorem and proof for arbitrary continuous infinite functions are presented, and the fitting ability of the HCF neuron 95 model is demonstrated.•HCFNN architecture based on the HCF neuron is proposed; it is used to mine specific feature representations and achieve adaptive parameter learning. Next, a novel adaptive optimization method for weights and hyper-parameters is proposed to achieve effective network learning. The 100 learned network model has better expression and learning ability with fewer neurons.•We conduct experiments on nine datasets in several domains, including the two-spirals problem, natural object recognition, face recognition, and person re-ID. Experimental results show that the proposed method has better 105 learning performance and generalization ability than the commonly used M-P and RBF neural networks. In addition, our method can improve the performance of various image recognition tasks and acquire good generalization.