𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Syntactic Methods in Pattern Recognition

✍ Scribed by K.S. Fu (Eds.)


Publisher
Academic Press
Year
1974
Tongue
English
Leaves
305
Series
Mathematics in Science and Engineering 112
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression. - Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering

✦ Table of Contents


Content:
Edited by
Page iii

Copyright page
Page iv

Preface
Pages ix-x

Acknowledgments
Page xi

Chapter 1 Introduction
Pages 1-24

Chapter 2 Introduction to Formal Languages
Pages 25-46

Chapter 3 Languages for Pattern Description
Pages 47-90

Chapter 4 Syntax Analysis as a Recognition Procedure
Pages 91-123

Chapter 5 Stochastic Languages and Stochastic Syntax Analysis
Pages 124-165

Chapter 6 Stochastic Languages for Syntactic Pattern Recognition
Pages 166-192

Chapter 7 Grammatical Inference for Syntactic Pattern Recognition
Pages 193-229

Appendix A Syntactic Recognition of Chromosome Patterns
Pages 231-235

Appendix B PDL (Picture Description Language)
Pages 236-244

Appendix C Syntactic Recognition of Two-Dimensional Mathematical Expressions
Pages 245-252

Appendix D Syntactic Description of Hand-Printed FORTRAN Characters
Pages 253-256

Appendix E Syntactic Recognition of Chinese Characters
Pages 257-261

Appendix F Syntactic Recognition of Spoken Words
Pages 262-268

Appendix G Plex Languages
Pages 269-277

Appendix H Web Grammars
Pages 278-282

Appendix I Tree Grammars for Syntactic Pattern Recognition
Pages 283-288

Author Index
Pages 289-293

Subject Index
Pages 294-295


πŸ“œ SIMILAR VOLUMES


Syntactic Methods in Pattern Recognition
✍ K.S. Fu (Eds.) πŸ“‚ Library πŸ“… 1974 πŸ› Academic Press 🌐 English

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrang

cover
✍ Flasiński, Mariusz, author πŸ“‚ Library πŸ“… 2019 πŸ› Singapore ; Hackensack, NJ : World Scientific Publ 🌐 English

1 online resource

Syntactic Pattern Recognition, Applicati
✍ K. S. Fu (auth.), Professor King Sun Fu PhD (eds.) πŸ“‚ Library πŸ“… 1977 πŸ› Springer-Verlag Berlin Heidelberg 🌐 English

<p>The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. In the decision-theoretic approach, aset of characteristic measurements, c

Hybrid Methods in Pattern Recognition
✍ H. Bunke, A. Kandel πŸ“‚ Library πŸ“… 2002 πŸ› World Scientific 🌐 English

Collection of articles describing recent progress in this emerging field. Covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, and others.