A Global Algorithm for Training Multilayer Neural Networks

2006/07/06 by Hong Zhao, Tao Jin, Zhao, Hong +1
Computer Science · #Biological Physics (physics.bio-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Face and Expression Recognition #Machine Learning and ELM #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.physics/0607046

openalex publication_date 2006/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a global algorithm for training multilayer neural networks in this Letter. The algorithm is focused on controlling the local fields of neurons induced by the input of samples by random adaptations of the synaptic weights. Unlike the backpropagation algorithm, the networks may have discrete-state weights, and may apply either differentiable or nondifferentiable neural transfer functions. A two-layer network is trained as an example to separate a linearly inseparable set of samples into two categories, and its powerful generalization capacity is emphasized. The extension to more general cases is straightforward.

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