A Literature Study Concerning Generalization Error Bounds for Neural Networks via Rademacher Complexity

Description

In this essay some fundamental results from the theory of machine learning and neural networks are presented, with the goal of finally discussing bounds on the generalization error of neural networks, via Rademacher complexity. / I denna uppsats presenteras några grundläggande resultat från teorin kring maskininlärning och neurala nätverk, med målet att slutligen diskutera övre begräsningar på generaliseringsfelet hos neurala nätverk, via Rademachers komplexitet.

Links & Downloads

1. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184487

Tags

machine

learning

neural

networks

generalization

error

rademacher

complexity

maskininlärning

neurala

nätverk

generaliseringfel

rademachers

komplexitet

Mathematics

Matematik

Probability Theory and Statistics

Sannolikhetsteori och statistik

Additional Fields

Identifer	oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-184487
Date	January 2021
Creators	Nordenfors, Oskar
Publisher	Umeå universitet, Institutionen för matematik och matematisk statistik
Source Sets	DiVA Archive at Upsalla University
Language	English
Detected Language	English
Type	Student thesis, info:eu-repo/semantics/bachelorThesis, text
Format	application/pdf
Rights	info:eu-repo/semantics/openAccess