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  3. Numerisk analyse

Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons

Af: Julian Knaup Engelsk Paperback

Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons

Af: Julian Knaup Engelsk Paperback
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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.
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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.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 77
ISBN-13: 9783658389543
Indbinding: Paperback
Udgave:
ISBN-10: 3658389540
Kategori: Numerisk analyse
Udg. Dato: 8 aug 2022
Længde: 0mm
Bredde: 148mm
Højde: 210mm
Forlag: Springer Fachmedien Wiesbaden
Oplagsdato: 8 aug 2022
Forfatter(e): Julian Knaup
Forfatter(e) Julian Knaup

Kategori Numerisk analyse

ISBN-13 9783658389543

Sprog Engelsk

Indbinding Paperback

Sider 77

Udgave

Længde 0mm

Bredde 148mm

Højde 210mm

Udg. Dato 8 aug 2022

Oplagsdato 8 aug 2022

Forlag Springer Fachmedien Wiesbaden

Kategori sammenhænge