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Student Number 79325007
Author Yuan-Kai Wang()
Author's Email Address No Public.
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Department Computer Science and Information Engineering
Year 1994
Semester 2
Degree Ph.D.
Type of Document Doctoral Dissertation
Language English
Title Pattern Recognition Applications of Genetic Algorithms
Date of Defense
Page Count 0
Keyword
  • Genetic Algorithms;Pattern Recognition;Subgraph Isomorphism
  • Abstract
    Table of Content COVER
    CHAPTER 1 INTRODUCTION
    1.1 Motivation
    1.1.1 Modeling of Pattern Recognition Problem
    1.1.2 Adoption of Genetic Algorithms
    1.2 Related Works
    1.2.1 Approaches in Pattern Recognition
    1.2.2 Simulated Annealing
    1.2.3 Progress of Applying Genetic Algorithms on Pattern Recognition
    1.3 Features of the Research
    1.4 Organization of the Dissertation
    CHAPTER 2 ERROR-CORRECTING GRAPH ISOMORPHISM
    2.1 Introduction
    2.2 Error-Correcting Graph Isomorphism (ECGI) Problem
    2.2.1 Earlier Approaches
    2.2.2 Problem Statement
    2.3 Genetic Optimization
    2.3.1 Representation and Operators
    2.3.2 Fitness Function
    2.4 Refined Genetic Optimization
    2.4.1 Status Matching
    2.4.2 Local Search Strategies
    2.4.3 Inhibitive Selection Operator
    2.5 Experimental Results
    2.5.1 Results of Genetic Optimization
    2.5.2 Results of Refined Genetic Optimization
    2.6 Concluding Remarks
    CHAPTER 3 ERROR-CORRECTING SUBGRAPH ISOMORPHISM
    3.1 Introduction
    3.2 Error-Correcting Subgraph Isomorphism (ECSI) Problems
    3.3 Genetic Optimization
    3.3.1 Representation and Operators
    3.3.2 Fitness Function
    3.4 Refined Genetic Optimization
    3.4.1 Assignment Algorithms
    3.4.2 Procedural Description of Refined Genetic Optimization
    3.5 Experimental Results
    3.5.1 Results of Genetic Optimization
    3.5.2 Results of Refined Genetic Optimization
    3.5.3 Complexity of the Proposed Optimization Algorithms
    3.6 Concluding Remarks
    CHAPTER 4 GEOMETRIC INVARIANT MATCHING
    4.1 Introduction
    4.2 Geometric Invariant Matching
    4.3 Genetic Optimization
    4.3.1 Representation
    4.3.2 Objective Function
    4.3.3 Genetic Operators
    4.3.4 Rates of Genetic Operators
    4.4 Experimental Results
    4.4.1 Global CharacterNormalizationProblem
    4.4.2 Characteristics of Global Character Normalization
    4.4.3 Template and Distorted Images of Characters
    4.4.4 Results
    4.5 Concluding Remarks
    CHAPTER 5 GENETIC SPARSE DISTRIBUTED MEMORY
    5.1 Introduction
    5.2 Sparse Distributed Memory (SDM)
    5.2.1 Writing operation of SDM
    5.2.2 Reading operation of SDM
    5.2.3 Problems of SDM in Pattern Recognition
    5.3 Genetic Sparse Distributed Memory (GSDM)
    5.4 Experimental Results
    5.4.1 Application on Handwritten Numeral Recognition
    5.4.1 Analysis of SDM
    5.4.3 Comparisons Between GSDM and SDM
    5.4.4 Other Characteristics of GSDM
    5.5
    CHAPTER 6 CONCLUSIONS
    6.1 Summary
    6.2 Conclusions
    6.3 Future Researches
    References
    APPENDIX A,B
    OTHERS
    Reference
    Advisor
  • Kuo-Chin Fan(SM)
  • Files No Any Full Text File.
    Date of Submission

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