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Titlebook: Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons; Julian Knaup Book 2022 The Editor(s) (if applicable)

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發(fā)表于 2025-3-21 18:57:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons
編輯Julian Knaup
視頻videohttp://file.papertrans.cn/463/462369/462369.mp4
叢書名稱BestMasters
圖書封面Titlebook: Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons;  Julian Knaup Book 2022 The Editor(s) (if applicable)
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
關(guān)鍵詞MLMVN; MVN; Class Assignment; Complex-Valued; Machine Learning; Classification; CVNN; Fourier Transform; Pha
版次1
doihttps://doi.org/10.1007/978-3-658-38955-0
isbn_softcover978-3-658-38954-3
isbn_ebook978-3-658-38955-0Series ISSN 2625-3577 Series E-ISSN 2625-3615
issn_series 2625-3577
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:29:01 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:32:48 | 只看該作者
https://doi.org/10.1007/978-3-658-38955-0MLMVN; MVN; Class Assignment; Complex-Valued; Machine Learning; Classification; CVNN; Fourier Transform; Pha
地板
發(fā)表于 2025-3-22 05:23:04 | 只看該作者
Introduction,ore, machine learning has recently gained popularity and importance. In particular, areas of pattern recognition and natural language processing have been revolutionized by Deep Learning [KSH12][HS97].
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發(fā)表于 2025-3-22 09:53:51 | 只看該作者
Preliminaries,are introduced in Chapter 2.1, and then the computational models of artificial neural networks are explored in more detail in Chapter 2.2. For this purpose, a brief review of the history of artificial neural networks is given, and their architecture and training are discussed.
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發(fā)表于 2025-3-22 23:14:51 | 只看該作者
Evaluation,ter 5.2 presents two datasets and examines them for their class similarity. Afterwards, Chapter 5.3 provides the results of the evaluation. Finally, Chapter 5.4 concludes with a discussion and Chapter 5.5 summarizes the evaluation.
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發(fā)表于 2025-3-23 02:30:48 | 只看該作者
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發(fā)表于 2025-3-23 06:59:36 | 只看該作者
Conclusion and Outlook,This chapter summarizes the entire thesis and its findings. In addition, an outlook is given on topics that require further consideration.
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