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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.
Remarks on Two Problems Connected with Pattern Recognition
Research on Pattern Recognition in France
Implications of Interactive Graphic Computers for Pattern Recognition Methodology
Statistical Analysis as a Tool to Make Patterns Emerge from Data
Pattern Recognition, The Challenge, Are We Meeting It?
Nonsupervised Learning in Statistical Pattern Recognition
Learning in Pattern Recognition
Parallel Computation in Pattern Recognition
Descriptive Pattern-Analysis Techniques: Potentialities and Problems
On Sequential Pattern Recognition Systems
Introduction to Biological and Mechanical Pattern Recognition
On the Automatic Classification of Fingerprints
Network Properties for Pattern Recognition
Goal-Directed Pattern Recognition
Cluster Formation at Various Perceptual Levels
Recognition, Machine 'Recognition'and Statistical Approaches
Pattern Recognition Applied to the Counting of Nerve Fiber Cross-Sections and Water Droplets
Recognition by Imitating the Process of Pattern Generation
Designing Pattern Categorizers with Extremal Paradigm Information
The Importance of Pattern Recognition for General Purpose Adjustment Systems
Recognition and Action
Some Views on Pattern-Recognition Methodology
The Evaluation of the Statistical Classifier
Adaptive System of Pattern Recognition
Nonparametric Learning and Pattern Recognition Using a Finite Number of States
Feature Selection for Pattern Recognition Systems
A Contribution to the Informational Analysis of Pattern
Pattern Recognition as an Inductive Process
Invariant Recognition of Geometric Shapes
- No. of pages:
- © Academic Press 1969
- 1st January 1969
- Academic Press
- eBook ISBN:
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