Syntactic methods in pattern recognition /
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...
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Main Authors: | |
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Corporate Authors: | |
Published: |
Academic Press,
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Publisher Address: | New York : |
Publication Dates: | 1974. |
Literature type: | eBook |
Language: | English |
Series: |
Mathematics in science and engineering ;
v. 112 |
Subjects: | |
Online Access: |
http://www.sciencedirect.com/science/bookseries/00765392/112 |
Summary: |
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. |
Carrier Form: | 1 online resource (xi, 295 pages) : illustrations. |
Bibliography: | Includes bibliographical references. |
ISBN: |
9780080956213 0080956211 |
Index Number: | Q327 |
CLC: | O235 |
Contents: |
Front Cover; Syntactic Methods in Pattern Recognition; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1 Introduction; 1.1 Syntactic (Structural) Approach to Pattern Recognition; 1.2 Syntactic Pattern Recognition System; 1.3 Preprocessing Techniques; 1.4 Pattern Segmentation; 1.5 Remarks on Syntactic Approach versus Decision-Theoretic Approach; References; Chapter 2 Introduction to Formal Languages; 2.1 Introduction; 2.2 Languages and Phrase-Structure Grammars; 2.3 Finite-State Languages and Finite-State Automata; 2.4 Context-Free Languages and Pushdown Automata. 2.5 Turing Machines and Linear-Bounded Automata2.6 Modified Grammars; References; Chapter 3 Languages for Pattern Description; 3.1 Selection of Pattern Primitives; 3.2 Pattern Grammar: Introduction; 3.3 High Dimensional Pattern Grammars; 3.4 The Use of Semantic Information; References; Chapter 4 Syntax Analysis as a Recognition Procedure; 4.1 Introduction; 4.2 Top-Down Parsing; 4.3 Bottom-Up Parsing; 4.4 LR(k) Grammars; 4.5 An Efficient Top-Down Parser; 4.6 Operator Precedence Grammars; 4.7 Precedence and Extended Precedence Grammars; 4.8 Syntax Analysis of Context-Free Programmed Languages. 6.3 Examples of Stochastic Syntax AnalysisReferences; Chapter 7 Grammatical Inference for Syntactic Pattern Recognition; 7.1 Introduction and Basic Definitions; 7.2 Grammatical Inference Algorithms by Enumeration; 7.3 Grammatical Inference Algorithms by Induction; 7.4 Bayesian Inference of Stochastic Grammars; 7.5 Synthesis of Stochastic Finite-State Automata; 7.6 A Practical Grammatical Inference System; 7.7 Approximation of Stochastic Languages; References; Appendix A Syntactic Recognition of Chromosome Patterns; Appendix B PDL (Picture Description Language). Appendix C Syntactic Recognition of Two-Dimensional Mathematical ExpressionsAppendix D Syntactic Description of Hand-Printed FORTRAN Characters; Appendix E Syntactic Recognition of Chinese Characters; Appendix F Syntactic Recognition of Spoken Words; Appendix G Plex Languages; Appendix H Web Grammars; Appendix I Tree Grammars for Syntactic Pattern Recognition; Author Index; Subject Index. |