Convergence Analysis of Online Gradient Method for High-Order Neural Networks and Their Sparse Optimization

Summary

This study introduces a smoothing technique for sigma-pi-sigma neural networks (SPSNNs) to improve network sparseness and generalization. The method enhances online gradient descent by optimizing network structure and controlling redundancy, supported by theoretical and experimental results.

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