Research on Generalized Hybrid Probability Convolutional Neural Network

Research on Generalized Hybrid Probability Convolutional Neural Network

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This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN.Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is proposed to solve abstract feature classification with an unknown distribution form.To copyright tiki mug measure the generalization ability of the CFM, a new index is defined and the positive correlation between it and the CFM is researched.Generally, a fully trained CFM can extract features that are beneficial to classification, regardless of whether the data participate in training the CFM.In the CNN, the fully connected layer in the 2334-080 MLP is not always optimal, and the extracted abstract feature has an unknown distribution.

Thus, an improved classifier called the structure-optimized probabilistic neural network (SOPNN) is used for abstract feature classification in the GHP-CNN.In the SOPNN, the separability information is not lost in the normalization process, and the final classification surface is close to the optimal classification surface under the Bayesian criterion.The proposed GHP-CNN utilizes the generalization ability of the CFM and the classification ability of the SOPNN.Experiments show that the proposed network has better classification ability than the existing hybrid neural networks.