Principal Component Neural Networks: Theory and Applications

Principal Component Neural Networks Theory and Applications Systematically explores the relationship between principal component analysis PCA and neural networks Provides a synergistic examination of the mathematical algorithmic application and architectural

  • Title: Principal Component Neural Networks: Theory and Applications
  • Author: Konstantinos Diamantaras
  • ISBN: 9780471054368
  • Page: 343
  • Format: Hardcover
  • Systematically explores the relationship between principal component analysis PCA and neural networks Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those whicSystematically explores the relationship between principal component analysis PCA and neural networks Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back propagation Examines the principles of biological perceptual systems to explain how the brain works Every chapter contains a selected list of applications examples from diverse areas.

    • [PDF] Download ☆ Principal Component Neural Networks: Theory and Applications | by ↠ Konstantinos Diamantaras
      343 Konstantinos Diamantaras
    • thumbnail Title: [PDF] Download ☆ Principal Component Neural Networks: Theory and Applications | by ↠ Konstantinos Diamantaras
      Posted by:Konstantinos Diamantaras
      Published :2018-08-11T11:02:02+00:00

    1 thought on “Principal Component Neural Networks: Theory and Applications”

    Leave a Reply

    Your email address will not be published. Required fields are marked *