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Least Squares - Optimization Techniques for Computer Vision: Least Squares Methods - cover

Least Squares - Optimization Techniques for Computer Vision: Least Squares Methods

Fouad Sabry

Maison d'édition: One Billion Knowledgeable

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Synopsis

What is Least Squares
 
The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals made in the results of each individual equation.
 
How you will benefit
 
(I) Insights, and validations about the following topics:
 
Chapter 1: Least squares
 
Chapter 2: Gauss-Markov theorem
 
Chapter 3: Regression analysis
 
Chapter 4: Ridge regression
 
Chapter 5: Total least squares
 
Chapter 6: Ordinary least squares
 
Chapter 7: Weighted least squares
 
Chapter 8: Simple linear regression
 
Chapter 9: Generalized least squares
 
Chapter 10: Linear least squares
 
(II) Answering the public top questions about least squares.
 
(III) Real world examples for the usage of least squares in many fields.
 
Who this book is for
 
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Least Squares.
Disponible depuis: 11/05/2024.
Longueur d'impression: 156 pages.

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