Extreme Learning Machine With Affine Transformation Inputs in an Activation Function

The extreme learning machine (ELM) has attracted much attention over the past decade due to its fast learning speed and convincing generalization performance. However, there still remains a practical issue to be approached when applying the ELM: the randomly generated hidden node parameters without...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 30; H. 7; S. 2093 - 2107
Hauptverfasser:	Cao, Jiuwen, Zhang, Kai, Yong, Hongwei, Lai, Xiaoping, Chen, Badong, Lin, Zhiping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Affine transformation (AT) activation function Affine transformations Algorithms Approximation algorithms Artificial neural networks Benchmark testing Benchmarks classification Datasets Distributed databases Entropy extreme learning machine (ELM) Image classification Learning algorithms Learning systems Maximum entropy Object recognition Parameters regression Robustness (mathematics) Scaling Training Tuning
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!