Subspace Learning of Neural Networks
Published on 3. September 2018
248 pages
978-1-4398-1536-6 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Researchers and Grad students working in Neural Networks, Signal Processing, Pattern Recognition.
Illustrations
84 s/w Abbildungen, 5 s/w Tabellen
5 Tables, black and white; 84 Illustrations, black and white
File size
10,76 MB
ISBN-13
978-1-4398-1536-6 (9781439815366)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jian Cheng Lv | Zhang Yi | Jiliu Zhou
Subspace Learning of Neural Networks
Book
06/2017
1st Edition
CRC Press
€80.65
Shipment within 10-20 days
Jian Cheng Lv | Zhang Yi | Jiliu Zhou
Subspace Learning of Neural Networks
Book
09/2010
1st Edition
CRC Press
€173.32
Shipment within 15-20 days
Persons
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.
Content
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.
