Operator theory, kernels, and Feedforward Neural Networks

• URL: http://arxiv.org/abs/2301.01327v1
• Date: Tue, 3 Jan 2023 19:30:31 GMT
• Title: Operator theory, kernels, and Feedforward Neural Networks
• Authors: Palle E. T. Jorgensen, Myung-Sin Song, and James Tian
• Abstract summary: We show how specific families of positive definite kernels serve as powerful tools in analyses of algorithms for multiple layer feedforward Neural Network models. Our focus is on particular kernels that adapt well to learning algorithms for data-sets/features which display intrinsic self-similarities at feedforward iteration of scaling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we show how specific families of positive definite kernels serve as powerful tools in analyses of iteration algorithms for multiple layer feedforward Neural Network models. Our focus is on particular kernels that adapt well to learning algorithms for data-sets/features which display intrinsic self-similarities at feedforward iterations of scaling.

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