Details

Autor(en) / Beteiligte

• Bauer, Benedikt
• Kohler, Michael

Titel

ON DEEP LEARNING AS A REMEDY FOR THE CURSE OF DIMENSIONALITY IN NONPARAMETRIC REGRESSION

Ist Teil von

• The Annals of statistics, 2019-08, Vol.47 (4), p.2261-2285

Ort / Verlag

Hayward: Institute of Mathematical Statistics

Erscheinungsjahr

2019

Quelle

Project Euclid Complete

Beschreibungen/Notizen

• Assuming that a smoothness condition and a suitable restriction on the structure of the regression function hold, it is shown that least squares estimates based on multilayer feedforward neural networks are able to circumvent the curse of dimensionality in nonparametric regression. The proof is based on new approximation results concerning multilayer feedforward neural networks with bounded weights and a bounded number of hidden neurons. The estimates are compared with various other approaches by using simulated data.