Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 8 of 57
Back to Result List

DropConnect in LVQ networks for regularization and confidence interval estimation

  • DropConnect (the generalization of Dropout) is a very simple regularization technique that was introduced a few years ago and has become extremely popular because of its simplicity and effectiveness. In this thesis, a suitable architecture for applying DropConnect to Learning Vector Quantization networks is proposed along with a reference implementation and experimental results. Inmany classification tasks, the uncertainty of themodel is a vital piece of information for experts. Methods to extract the uncertainty and stability using DropConnect are also proposed and the corresponding experimental results are documented.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar


Author:Jensun Richie Dinesh John Ravichandran
Advisor:Thomas Villmann, Marika Kaden
Document Type:Bachelor Thesis
Year of Completion:2018
Granting Institution:Hochschule Mittweida
Release Date:2019/04/30
GND Keyword:Vektorquantisierung; Regularisierung
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
Dewey Decimal Classification:621.3822 digitale Signalverarbeitung
Open Access:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG