Approximation Theory and Algorithms for Data Analysis

Höfundur Armin Iske

Útgefandi Springer Nature

Snið Page Fidelity

Print ISBN 9783030052270

Útgáfa 0

Útgáfuár 2018

2.490 kr.

Description

Efnisyfirlit

  • Preface
  • Table of Contents
  • 1 Introduction
  • 1.1 Preliminaries, Definitions and Notations
  • 1.2 Basic Problems and Outlook
  • 1.3 Approximation Methods for Data Analysis
  • 1.4 Hints on Classical and More Recent Literature
  • 2 Basic Methods and Numerical Algorithms
  • 2.1 Linear Least Squares Approximation
  • 2.2 Regularization Methods
  • 2.3 Interpolation by Algebraic Polynomials
  • 2.4 Divided Differences and the Newton Representation
  • 2.5 Error Estimates and Optimal Interpolation Points
  • 2.6 Interpolation by Trigonometric Polynomials
  • 2.7 The Discrete Fourier Transform
  • 3 Best Approximations
  • 3.1 Existence
  • 3.2 Uniqueness
  • 3.3 Dual Characterization
  • 3.4 Direct Characterization
  • 3.5 Exercises
  • 4 Euclidean Approximation
  • 4.1 Construction of Best Approximations
  • 4.2 Orthogonal Bases and Orthogonal Projections
  • 4.3 Fourier Partial Sums
  • 4.4 Orthogonal Polynomials
  • 4.5 Exercises
  • 5 Chebyshev Approximation
  • 5.1 Approaches to Construct Best Approximations
  • 5.2 Strongly Unique Best Approximations
  • 5.3 Haar Spaces
  • 5.4 The Remez Algorithm
  • 5.5 Exercises
  • 6 Asymptotic Results
  • 6.1 The Weierstrass Theorem
  • 6.2 Complete Orthogonal Systems and Riesz Bases
  • 6.3 Convergence of Fourier Partial Sums
  • 6.4 The Jackson Theorems
  • 6.5 Exercises
  • 7 Basic Concepts of Signal Approximation
  • 7.1 The Continuous Fourier Transform
  • 7.2 The Fourier Transform on L2(R)
  • 7.3 The Shannon Sampling Theorem
  • 7.4 The Multivariate Fourier Transform
  • 7.5 The Haar Wavelet
  • 7.6 Exercises
  • 8 Kernel-based Approximation
  • 8.1 Multivariate Lagrange Interpolation
  • 8.2 Native Reproducing Kernel Hilbert Spaces
  • 8.3 Optimality of the Interpolation Method
  • 8.4 Orthonormal Systems, Convergence, and Updates
  • 8.5 Stability of the Reconstruction Scheme
  • 8.6 Kernel-based Learning Methods
  • 8.7 Exercises
  • 9 Computerized Tomography
  • 9.1 The Radon Transform
  • 9.2 The Filtered Back Projection
  • 9.3 Construction of Low-Pass Filters
  • 9.4 Error Estimates and Convergence Rates
  • 9.5 Implementation of the Reconstruction Method
  • 9.6 Exercises
  • References
  • Subject Index
  • Name Index
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