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GMLVQ Networks with Non-Linear Mapping

  • Prototype-based classification methods like Generalized Matrix Learning Vector Quantization (GMLVQ) are simple and easy to implement. An appropriate choice of the activation function plays an important role in the performance of (deep) multilayer perceptrons (MLP) that rely on a non-linearity for classification and regression learning. In this thesis, successful candidates of non-linear activation functions are investigated which are known for MLPs for application in GMLVQ to realize a non-linear mapping. The influence of the non-linear activation functions on the performance of the model with respect to accuracy, convergence rate are analyzed and experimental results are documented.

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Metadaten
Author:Girish Kumar Doddagubbi Ramesh
Document Type:Master's Thesis
Language:English
Year of Completion:2019
Granting Institution:Hochschule Mittweida
Release Date:2021/03/05
GND Keyword:Vector Association; Zeitreihe; Algorithmus; Maschinelles Lernen
Note:
Printexemplar Präsenzbestand
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:006.31 Maschinelles Lernen
Open Access:Innerhalb der Hochschule
Licence (German):License LogoUrheberrechtlich geschützt