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Methodologies of Pattern Recognition
About This Book
Methodologies of Pattern Recognition is a collection of papers that deals with the two approaches to pattern recognition (geometrical and structural), the Robbins-Monro procedures, and the implications of interactive graphic computers for pattern recognition methodology. Some papers describe non-supervised learning in statistical pattern recognition, parallel computation in pattern recognition, and statistical analysis as a tool to make patterns emerge from data. One paper points out the importance of cluster processing in visual perception in which proximate points of similar brightness values form clusters. At higher levels of mental activity humans are efficient in clumping complex items into clusters. Another paper suggests a recognition method which combines versatility and an efficient noise-proofness in dealing with the two main problems in the field of recognition. These difficulties are the presence of a large variety of observed signals and the presence of interference. One paper reports on a possible feature selection for pattern recognition systems employing the minimization of population entropy. Electronic engineers, physicists, physiologists, psychologists, logicians, mathematicians, and philosophers will find great rewards in reading the above collection.
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Table of contents
- Front Cover
- Methodologies of Pattern Recognition
- Copyright Page
- Table of Contents
- CONTRIBUTORS
- PREFACE
- CHAPTER 1. REMARKS ON TWO PROBLEMS CONNECTED WITH PATTERN RECOGNITION
- CHAPTER 2. RESEARCH ON PATTERN RECOGNITION IN FRANCE
- CHAPTER 3. IMPLICATIONS OF INTERACTIVE GRAPHIC COMPUTERS FOR PATTERN RECOGNITION METHODOLOGY
- CHAPTER 4. STATISTICAL ANALYSIS AS A TOOL TO MAKE PATTERNS EMERGE FROM DATA
- CHAPTER 5. PATTERN RECOGNITION, THE CHALLENGE, ARE WE MEETING IT?
- CHAPTER 6. NONSUPERVISED LEARNING IN STATISTICAL PATTERN RECOGNITION
- CHAPTER 7. LEARNING IN PATTERN RECOGNITION
- CHAPTER 8. PARALLEL COMPUTATION IN PATTERN RECOGNITION
- CHAPTER 9. DESCRIPTIVE PATTERN-ANALYSIS TECHNIQUES: POTENTIALITIES AND PROBLEMS
- CHAPTER 10. ON SEQUENTIAL PATTERN RECOGNITION SYSTEMS
- CHAPTER 11. INTRODUCTION TO BIOLOGICAL AND MECHANICAL PATTERN RECOGNITION
- CHAPTER 12. ON THE AUTOMATIC CLASSIFICATION OF FINGERPRINTS -SOME CONSIDERATIONS ON THE LINGUISTIC INTERPRETATION OF PICTURES
- CHAPTER 13. NETWORK PROPERTIES FOR PATTERN RECOGNITION
- CHAPTER 14. GOAL-DIRECTED PATTERN RECOGNITION
- CHAPTER 15. CLUSTER FORMATION AT VARIOUS PERCEPTUAL LEVELS
- CHAPTER 16. RECOGNITION, MACHINE "RECOGNITION", AND STATISTICAL APPROACHES
- CHAPTER 17. PATTERN RECOGNITION APPLIED TO THE COUNTING OF NERVE FIBER CROSS-SECTIONS AND WATER DROPLETS
- CHAPTER 18. RECOGNITION BY IMITATING THE PROCESS OF PATTERN GENERATION
- CHAPTER 19. DESIGNING PATTERN CATEGORIZERS WITH EXTREMAL PARADIGM INFORMATION
- CHAPTER 20. THE IMPORTANCE OF PATTERN RECOGNITION FOR GENERAL PURPOSE ADJUSTMENT SYSTEMS
- CHAPTER 21. RECOGNITION AND ACTION
- CHAPTER 22. SOME VIEWS ON PATTERN—RECOGNITION METHODOLOGY
- CHAPTER 23. THE EVALUATION OF THE STATISTICAL CLASSIFIER
- CHAPTER 24. ADAPTIVE SYSTEM OF PATTERN RECOGNITION
- CHAPTER 25. NONPARAMETRIC LEARNING AND PATTERN RECOGNITION USING A FINITE NUMBER OF STATES
- CHAPTER 26. FEATURE SELECTION FOR PATTERN RECOGNITION SYSTEMS
- CHAPTER 27. A CONTRIBUTION TO THE INFORMATIONAL ANALYSIS OF PATTERN
- CHAPTER 28. PATTERN RECOGNITION AS AN INDUCTIVE PROCESS
- CHAPTER 29. INVARIANT RECOGNITION OF GEOMETRIC SHAPES
- COMMENTS
- NAME INDEX
- SUBJECT INDEX