arXiv Open Access 2020

On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data

Michael Kohler Adam Krzyzak

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Abstrak

A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is able to circumvent the curse of dimensionality.

Topik & Kata Kunci

stat.ML cs.LG

Penulis (2)

Michael Kohler

Adam Krzyzak

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APA MLA BibTeX

Kohler, M., Krzyzak, A. (2020). On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data. https://arxiv.org/abs/2011.00328

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓