HELPR: Hybrid Evolutionary Learning for Pattern Recognition
Final rept. 30 Apr 1999-30 Nov 2005
WRIGHT STATE UNIV DAYTON OH DEPT OF COMPUTER SCIENCE AND ENGINEERING
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The availability of inexpensive sensors coupled with the rise of the internet has led to a rapid expansion in the amount of data available for analysis. Although there are a myriad of uses for this data, one of the most common applications is pattern recognition. The traditional approach to creating pattern recognition systems is human intensive requiring experts with training in pattern recognition to collaborate with experts who have knowledge of the problem domain to develop a custom recognition system for a specific problem. In contrast, the HELPR software architecture has focused on the design and implementation of software tools and techniques that use evolutionary computation to synthesize target systems from raw data, thereby reducing the personnel requirements and time needed to deploy new recognition systems. Result produce by ATR systems evolved using HELPR are reported for a variety of tasks involving HRR, SAR, E3D, and image processing.
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