A novel memory-based pattern recognition system

WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
Andrew Kerfonta (Creator)
Western Carolina University (WCU )
Web Site: http://library.wcu.edu/
Peter Tay

Abstract: This thesis proposes a novel method for learning and pattern recognition. The algorithm presented relies entirely on memory arranged in a custom hierarchical data structure which shifts the workload from the processor to memory. The structure and functionality draw on biology and neuroscience for inspiration while not losing sight of the inherent strengths and limitations of modern computers. A hierarchy of learned nodes is built, stored, and used for recognition without the need for complicated math or statistics. Recognition and prediction are inherent to the hierarchy and require little additional computation, even for matching of partial patterns. The experiments and results presented empirically demonstrate the robustness of memory-based recognition of images.

Additional Information

Language: English
Date: 2013
AI, human learning, memory, pattern recognition, prediction, representation
Pattern recognition systems
Memory hierarchy (Computer science)

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