Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons (Paperback, 1st ed. 2022)

Multilayer neural networks based on multi-valued neurons (MLMVNs) have been proposed to combine the advantages of complex-valued neural networks with a plain derivative-free learning algorithm. In addition, multi-valued neurons (MVNs) offer a multi-valued threshold logic resulting in the ability to replace multiple conventional output neurons in classification tasks. Therefore, several classes can be assigned to one output neuron. This book introduces a novel approach to assign multiple classes to numerous MVNs in the output layer. It was found that classes that possess similarities should be allocated to the same neuron and arranged adjacent to each other on the unit circle. Since MLMVNs require input data located on the unit circle, two employed transformations are reevaluated. The min-max scaler utilizing the exponential function, and the 2D discrete Fourier transform restricting to the phase information for image recognition. The evaluation was performed on the Sensorless Drive Diagnosis dataset and the Fashion MNIST dataset.

General

Imprint:	Springer Vieweg
Country of origin:	Germany
Series:	BestMasters
Release date:	August 2022
First published:	2022
Authors:	Julian Knaup
Dimensions:	210 x 148mm (L x W)
Format:	Paperback
Pages:	77
Edition:	1st ed. 2022
ISBN-13:	978-3-658-38954-3
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	3-658-38954-0
Barcode:	9783658389543

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