Mutual information derived tree structure in an adaptive pattern recognition system
Abstract
Abstract AN ADAPTIVE PATTERN RECOGNITION SYSTEM IS PROVIDED WHICH CALCULATES THE MUTUAL INFORMATION PROVIDED BY PAIRS OF FEATURES EXTRACTED BY A FEATURE EXTRACTING DEVICE. THE RELATIVE MAGNITUDES OF MUTUAL INFORMATION ARE DETECTED SERIATIM AND A CLOSED LOOP AVOIDANCE MODULE PREVENTS FORMING A CLOSED LOOP, TO RETAIN A STATISTICAL TREE RELATIONSHIP. PATTERN LOGIC STORES THE SET OF PAIRS HAVING HIGHEST VALUES OF MUTUAL INFORMATION. THEN THE SYSTEM IS PREPARED TO OPERATE A RECOGNITION SYSTEM. THE INDIVIDUAL FEATURES ARE WEIGHTED, ACCORDING TO STATISTICAL ANALYSIS, BY ANALOGUE COMPUTERS. ALSO, THE PAIRS OF INFORMATION ARE GATED AND WEIGHTED FOR EACH PATTERN IN ACCORDANCE WITH STATISTICAL WEIGHTING PRINCIPLES. THE SUMMING NETWORK FOR A PLURALITY OF PATTERNS ARE COMPARED IN A MAXIMUM DETECTOR FOR ULTIMATE RECOGNITION OF THE MOST LIKELY PATTERN IDENTIFICATION.
Inventors
- 👤 Chao K Chow
- 👤 Chao N Liu
Bibliographic Details
Claims Analysis
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