Description: Principal Component Analysis Networks and Algorithms, Hardcover by Kong, Xiangyu; Hu, Changhua; Duan, Zhansheng, ISBN 981102913X, ISBN-13 9789811029134, Like New Used, Free shipping in the US This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, ., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, th only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
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Book Title: Principal Component Analysis Networks and Algorithms
Number of Pages: Xxii, 323 Pages
Language: English
Publication Name: Principal and Minor Component Analysis Neural Networks to Principal Component Analysis Networks and Algorithms
Publisher: Springer
Subject: Engineering (General), Intelligence (Ai) & Semantics, Neural Networks, Computer Vision & Pattern Recognition
Publication Year: 2017
Type: Textbook
Item Weight: 227.4 Oz
Subject Area: Computers, Technology & Engineering
Author: Changhua Hu, Xiangyu Kong, Zhansheng Duan
Item Length: 9.3 in
Item Width: 6.1 in
Format: Hardcover
