Subspace Learning of Neural Networks

1. Auflage 2010
ISBN: 978-1-4398-1535-9
Verlag: CRC Press

Buch, Englisch, 256 Seiten, Format (B × H): 156 mm x 235 mm, Gewicht: 498 g

Reihe: Automation and Control Engineering

Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

Zielgruppe

Researchers and Grad students working in Neural Networks, Signal Processing, Pattern Recognition.

Autoren/Hrsg.

Lv, Jian Cheng

Yi, Zhang

Zhou, Jiliu

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Überwachungstechnik

Inhaltsverzeichnis

Introduction. PCA Learning Algorithms with Constants Learning Rates. PCA Learning Algorithms with Adaptive Learning Rates. GHA PCA Learning Algorithms. MCA Learning Algorithms. ICA Learning Algorithms. Chaotic Behaviors Arising from Learning Algorithms. Multi-Block-Based MCA for Nonlinear Surface Fitting. A ICA Algorithm for Extracting Fetal Electrocardiogram. Some Applications of PCA Neural Networks. Conclusion.

Über Autor(innen)

Jian Cheng LV and Zhang Yi are affiliated with the Machine Intelligence Lab of the College of Computer Science at Sichuan University. Jiliu Zhou is affiliated with the College of Computer Science at Sichuan University.