| 书目名称 | Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons |
| 编辑 | Julian Knaup |
| 视频video | |
| 丛书名称 | BestMasters |
| 图书封面 |  |
| 描述 | 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. |
| 出版日期 | Book 2022 |
| 关键词 | MLMVN; MVN; Class Assignment; Complex-Valued; Machine Learning; Classification; CVNN; Fourier Transform; Pha |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-658-38955-0 |
| isbn_softcover | 978-3-658-38954-3 |
| isbn_ebook | 978-3-658-38955-0Series ISSN 2625-3577 Series E-ISSN 2625-3615 |
| issn_series | 2625-3577 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies |
发表于 2025-3-21 18:57:01
