Subspace Learning of Neural Networks Automation and Control Engineering Series
Auteurs : Cheng Lv Jian, Yi Zhang, Zhou Jiliu
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.
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.
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.
Date de parution : 06-2017
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 61,25 €
Ajouter au panierDate de parution : 10-2010
Ouvrage de 250 p.
15.6x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 160,25 €
Ajouter au panierThèmes de Subspace Learning of Neural Networks :
Mots-clés :
Conditional Expectation; PCA Learning Algorithms; Data Set; Jian Cheng; Symmetric Nonnegative Definite Matrix; Zhang Yi; Subspace Learning Algorithms; Jiliu Zhou; ECG Signal; MCA Learning Algorithms; Constant Learning Rates; ICA Learning Algorithms; Ellipsoid Segment; Invariant Set; PCA Model; Adaptive Learning Rate; Principal Component Directions; Subspace Learning; Principal Direction; Initial Vector; Equilibrium Points; fP Rin; Unit Eigenvector; Block Algorithms; Learning Rates; Direction Cosine; Intrinsic Dimensionality; Kernel PCA; Local PCA; Generalize PCA; Low Dimensional Model