Syntactic Methods in Pattern Recognition - 1st Edition - ISBN: 9780122695605, 9780080956213

Syntactic Methods in Pattern Recognition, Volume 112

1st Edition

Series Editors: K. S. Fu
Hardcover ISBN: 9780122695605
eBook ISBN: 9780080956213
Imprint: Elsevier Science
Published Date: 28th January 1974
Page Count: 322
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Table of Contents

Preface
Contents
1 Overview
I Methods of Operator Approximation in System Modelling
2 Nonlinear Operator Approximation with Preassigned Accuracy
2.1 Introduction
2.2 Generic formulation of the problem
2.3 Operator approximation in space C([0; 1]):
2.4 Operator approximation in Banach spaces by polynomial operators
2.5 Approximation on compact sets in topological vector spaces
2.6 Approximation on noncompact sets in Hilbert spaces
2.7 Special results for maps into Banach spaces
2.8 Concluding remarks
3 Interpolation of Nonlinear Operators 65
3.1 Introduction
3.2 Lagrange interpolation in Banach spaces
3.3 Weak interpolation of nonlinear operators
3.4 Some related results
3.5 Concluding remarks
4 Realistic Operators and their Approximation
4.1 Introduction
4.2 Formalization of concepts related to description of real-world objects
4.3 Approximation of R¡continuous operators
4.4 Concluding remarks
5 Methods of Best Approximation for Nonlinear Operators
5.1 Introduction
5.2 Best Approximation of nonlinear operators in Banach spaces: Deterministic case
5.3 Estimation of mean and covariance matrix for random vectors
5.4 Best Hadamard-quadratic approximation
5.5 Best polynomial approximation
5.6 Best causal approximation
5.7 Best hybrid approximations
5.8 Concluding remarks
II Optimal Estimation of Random Vectors
6 Computational Methods for Optimal Filtering of Stochastic Signals
6.1 Introduction
6.2 Optimal linear Filtering in Finite dimensional vector spaces
6.3 Optimal linear Filtering in Hilbert spaces
6.4 Optimal causal linear Filtering with piecewise constant memory
6.5 Optimal causal polynomial Filtering with arbitrarily variable memory
6.6 Optimal nonlinear Filtering with no memory constraint
6.7 Concluding remarks
7 Computational Methods for Optimal Compression and Reconstruction of Random Data
7.1 Introduction
7.2 Standard Principal Component Analysis and Karhunen-Loeeve transform (PCA{KLT)
7.3 Rank-constrained matrix approximations
7.4 Generic PCA{KLT
7.5 Optimal hybrid transform based on Hadamard-quadratic approximation
7.6 Optimal transform formed by a combination of nonlinear operators
7.7 Optimal generalized hybrid transform
7.8 Concluding remarks
Bibliography
Index


Description

Preface
Contents
1 Overview
I Methods of Operator Approximation in System Modelling
2 Nonlinear Operator Approximation with Preassigned Accuracy
2.1 Introduction
2.2 Generic formulation of the problem
2.3 Operator approximation in space C([0; 1]):
2.4 Operator approximation in Banach spaces by polynomial operators
2.5 Approximation on compact sets in topological vector spaces
2.6 Approximation on noncompact sets in Hilbert spaces
2.7 Special results for maps into Banach spaces
2.8 Concluding remarks
3 Interpolation of Nonlinear Operators 65
3.1 Introduction
3.2 Lagrange interpolation in Banach spaces
3.3 Weak interpolation of nonlinear operators
3.4 Some related results
3.5 Concluding remarks
4 Realistic Operators and their Approximation
4.1 Introduction
4.2 Formalization of concepts related to description of real-world objects
4.3 Approximation of R¡continuous operators
4.4 Concluding remarks
5 Methods of Best Approximation for Nonlinear Operators
5.1 Introduction
5.2 Best Approximation of nonlinear operators in Banach spaces: Deterministic case
5.3 Estimation of mean and covariance matrix for random vectors
5.4 Best Hadamard-quadratic approximation
5.5 Best polynomial approximation
5.6 Best causal approximation
5.7 Best hybrid approximations
5.8 Concluding remarks
II Optimal Estimation of Random Vectors
6 Computational Methods for Optimal Filtering of Stochastic Signals
6.1 Introduction
6.2 Optimal linear Filtering in Finite dimensional vector spaces
6.3 Optimal linear Filtering in Hilbert spaces
6.4 Optimal causal linear Filtering with piecewise constant memory
6.5 Optimal causal polynomial Filtering with arbitrarily variable memory
6.6 Optimal nonlinear Filtering with no memory constraint
6.7 Concluding remarks
7 Computational Methods for Optimal Compression and Reconstruction of Random Data
7.1 Introduction
7.2 Standard Principal Component Analysis and Karhunen-Loeeve transform (PCA{KLT)
7.3 Rank-constrained matrix approximations
7.4 Generic PCA{KLT
7.5 Optimal hybrid transform based on Hadamard-quadratic approximation
7.6 Optimal transform formed by a combination of nonlinear operators
7.7 Optimal generalized hybrid transform
7.8 Concluding remarks
Bibliography
Index

Key Features

  • Best operator approximation,
  • Non-Lagrange interpolation,
  • Generic Karhunen-Loeve transform
  • Generalised low-rank matrix approximation
  • Optimal data compression
  • Optimal nonlinear filtering

Readership

This book is intended for: Applied mathematicians and Electrical engineers And: Statisticians


Details

No. of pages:
322
Language:
English
Copyright:
© Elsevier Science 1974
Published:
Imprint:
Elsevier Science
eBook ISBN:
9780080956213
Hardcover ISBN:
9780122695605

About the Series Editors

K. S. Fu Series Editor

Affiliations and Expertise

School of Electrical Engineering Purdue University West Lafayette, Indiana