Syntactic pattern recognition (Record no. 35266)

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fixed length control field 06704nam a22001817a 4500
003 - CONTROL NUMBER IDENTIFIER
control field CUTN
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20210706161724.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210706b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789813278462
041 ## - LANGUAGE CODE
Language English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.4
Item number FLA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Flasiński, Mariusz
245 ## - TITLE STATEMENT
Title Syntactic pattern recognition
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Singapore
Name of publisher, distributor, etc World Scientific
Date of publication, distribution, etc 2019
505 ## - FORMATTED CONTENTS NOTE
Title Contents<br/>Introductory Issues<br/>1. Paradigmatic Considerations on Syntactic Pattern Recognition<br/>1.1 Structure - Property of Natural Objects and Artifacts<br/>1.2 Syntax - Rules to Arrange Well-Formed Structures<br/>1.3 Syntactic Pattern Recognition: When and Why<br/>1.3.1 Structure-Based Distinguishability of Objects<br/>1.3.2 Reusability of Structural Subpatterns<br/>1.3.3 Hierarchy-Oriented Multilevel Recognition<br/>1.3.4 Requirement of Structure-Based Interpretation<br/>2. Methodology of Syntactic Pattern Recognition<br/>2.1 Structural Patterns: Strings, Trees and Graphs<br/>2.2 Formal Tools: Grammars - Automata - Induction Algorithms<br/>2.3 Place of Syntactic Pattern Recognition in Computer Science<br/>String-based Models<br/>3. Pattern Recognition Based on Regular and CF Grammars<br/>3.1 Recognition with Finite-State Automata<br/>3.2 Recognition of CF Languages<br/>3.2.1 LL(k) Languages<br/>3.2.2 LR(k) Languages<br/>3.2.3 Precedence Parsing<br/>3.2.4 Earley Parser<br/>3.2.5 Cocke-Younger-Kasami Parser<br/>3.2.6 Summary of Parsers for CF Languages<br/>3.3 Recognition of Vague/Distorted Patterns<br/>3.3.1 Stochastic Languages<br/>3.3.2 Fuzzy Languages<br/>3.3.3 Error-Correcting Parsing<br/>3.3.4 Hidden Markov Model (HMM) and Viterbi Algorithm<br/>4. Enhanced String-Based Models for Pattern Recognition<br/>4.1 Grammars with Operator Controlled Derivations<br/>4.1.1 Indexed and Linear Indexed Grammars<br/>4.1.2 Head Grammars<br/>4.1.3 Combinatory Categorial Grammars<br/>4.1.4 Conjunctive and Boolean Grammars<br/>4.1.5 Remarks on Mildly Context-Sensitive Grammars<br/>4.2 Grammars with Programmed Derivations<br/>4.2.1 Programmed Grammars<br/>4.2.2 Dynamically Programmed Grammars<br/>4.3 Augmented Regular Expressions<br/>4.4 Attributed Grammars<br/>4.5 Picture Languages<br/>4.5.1 Picture Description Languages<br/>4.5.2 Two-Dimensional Automata<br/>4.5.3 Siromoney Matrix/Array Grammars<br/>4.5.4 Shape Grammars<br/>4.5.5 Shape Feature Languages<br/>4.5.6 Remarks on Picture and Visual Languages<br/>4.6 Timed Automata<br/>5. Inference (Induction) of String Languages<br/>5.1 Text Learning Methods for Regular Languages<br/>5.1.1 Brzozowski-Derivative-Based Inference<br/>5.1.2 k-Tail-Based Inference<br/>5.1.3 Inference of k-Testable Languages<br/>5.1.4 Inference of Reversible Regular Languages<br/>5.2 Informed Learning Methods for Regular Languages<br/>5.2.1 Gold-Trakhtenbrot-Bārzdiņš Model<br/>5.2.2 Algorithm RPNI<br/>5.3 Inference of Reversible CF Languages<br/>5.4 Baum-Welch Learning of HMMs<br/>5.5 Remarks on the Inference of String Languages<br/>6. Applications of String Methods<br/>6.1 Shape and Picture Analysis<br/>6.2 Optical Character Recognition<br/>6.3 Structure Analysis in Bioinformatics<br/>6.4 Medical Image/Signal Analysis<br/>6.5 Speech Recognition and NLP<br/>6.6 Analysis of Visual Events and Activities<br/>6.7 Signal Analysis for Process Monitoring and Control<br/>6.8 Architectural Object Analysis<br/>6.9 Feature Recognition for CAD/CAM<br/>6.10 Structure Analysis in Chemistry<br/>6.11 Radar Signal Analysis<br/>6.12 Pattern Recognition in Seismology<br/>6.13 Pattern Recognition in Geology<br/>6.14 Fingerprint Recognition<br/>6.15 Financial/Economics Time Series Analysis<br/>6.16 Image Processing<br/>6.17 Summary<br/>Tree-based Models<br/>7. Pattern Recognition Based on Tree Languages<br/>7.1 Recognition of Expansive Tree Languages<br/>7.2 Minimum-Distance SPECTA Model<br/>7.3 Maximum-Likelihood SPECTA Model<br/>7.4 Generalized Error-Correcting Tree Automata (GECTA)<br/>7.5 Tree Adjoining Grammars<br/>7.6 Remarks on Tree Languages<br/>8. Inference (Induction) of Tree Languages<br/>8.1 k-Tail-Based Inference of Tree Languages<br/>8.2 Inference of Reversible Tree Languages<br/>8.3 Tree-Derivative-Based Inference<br/>8.4 Informed Learning of Tree Languages<br/>8.5 Remarks on the Inference of Tree Languages<br/>9. Applications of Tree Methods<br/>9.1 Image Analysis and Processing<br/>9.2 Optical Character Recognition<br/>9.3 Structure Analysis in Bioinformatics<br/>9.4 Texture Analysis<br/>9.5 Analysis of Visual Events and Activities<br/>9.6 Pattern Recognition in Seismology<br/>9.7 Speech Recognition and NLP<br/>9.8 Summary<br/>Graph-based Models<br/>10. Pattern Analysis with Graph Grammars<br/>10.1 Bunke Attributed Programmed Graph Grammars<br/>10.2 Shi-Fu Parser for Expansive Graph Languages<br/>10.3 Sanfeliu-Fu Parser for Attributed Tree-Graph Grammars<br/>10.4 Recognition of ETPL(k) and ETPR(k) Graph Languages<br/>10.5 Recognition of Plex Languages<br/>10.5.1 Bunke-Haller Parser<br/>10.5.2 Peng-Yamamoto-Aoki Parser<br/>10.6 And-Or Graph Model<br/>10.7 Remarks on Graph Languages and Their Parsing<br/>11. Inference (Induction) of Graph Languages<br/>11.1 Inference of Expansive Graph Languages<br/>11.2 Inference of ETPL(k) Graph Languages<br/>11.2.1 Inference from IE Graph<br/>11.2.2 Inference from a Sample of IE Graphs<br/>11.3 Remarks on the Inference of Graph Languages<br/>12. Applications of Graph Methods<br/>12.1 Scene Analysis<br/>12.2 Picture and Diagram Analysis<br/>12.3 Feature Recognition for CAD/CAM<br/>12.4 Analysis of Visual Events and Activities<br/>12.5 Structure Analysis in Chemistry<br/>12.6 Optical Character Recognition<br/>12.7 Structure Analysis for Process Monitoring and Control<br/>12.8 Structure Analysis in Bioinformatics and Medicine<br/>12.9 Summary<br/>Future of Syntactic Pattern Recognition<br/>13. Summary of Results and Open Problems<br/>13.1 Summary of Results<br/>13.1.1 Theoretical Results<br/>13.1.2 Application Results<br/>13.2 Open Problems<br/>14. Methodological Issues<br/>14.1 General Methodological Principles<br/>14.2 Model-Specific Methodological Recommendations<br/>14.3 Concluding Remarks<br/>Appendix A Formal Languages and Automata - Selected Notions<br/>A.1 Chomsky’s String Grammars<br/>A.2 String Automata<br/>A.3 NLC Graph Grammars<br/>Bibliography<br/>Index
520 ## - SUMMARY, ETC.
Summary, etc This unique compendium presents the major methods of recognition and learning used in syntactic pattern recognition from the 1960s till 2018. Each method is introduced firstly in a formal way. Then, it is explained with the help of examples and its algorithms are described in a pseudocode. The survey of the applications contains more than 1,000 sources published since the 1960s. The open problems in the field, the challenges and the determinants of the future development of syntactic pattern recognition are discussed.This must-have volume provides a good read and serves as an excellent source of reference materials for researchers, academics, and postgraduate students in the fields of pattern recognition, machine perception, computer vision and artificial intelligence.
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    Dewey Decimal Classification     Non-fiction CUTN Central Library CUTN Central Library Generalia 06/07/2021   006.4 FLA 44015 06/07/2021 06/07/2021 General Books

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