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<p>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.</p>

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Operator theory, kernels, and feedforward neural networks

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Palle E. T. Jorgensen,
  • Myung-Sin Song,
  • James Tian

摘要

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.