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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.

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Metadaten
Author:Jensun Richie Dinesh John Ravichandran
Advisor:Thomas Villmann, Marika Kaden
Document Type:Bachelor Thesis
Language:English
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
Access Rights:Frei zugänglich
Licence (German):License LogoEs gilt das UrhG