{ "item_title" : "An Investigation of the Effects of Correlation and Autocorrelation in Classifier Fusion With Non-Declarations", "item_author" : [" Frank M. Mindrup "], "item_description" : "Air Force doctrine requires reliable and accurate information when striking targets. Further, this doctrine states that fusion should be utilized whenever possible to ensure the best possible information is conveyed; there is no specific guidance as to how to fuse this information. This thesis extends the research found in Leap, Bauer, and Oxley (2004) to include a non-declared class. The Identification system operating characteristic (ISOC) was adapted to allow for non-declarations both at the individual sensor level as well as the fused output level. A probabilistic neural network (PNN) was also used as a fusion technique. A cost function was developed that incorporated misclassification error as well as non-declaration rules. In addition, a heuristic was developed to find optimal rules through a likelihood ratio method. Finally, a sensitivity analysis was performed.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/28/830/726/1288307268_b.jpg", "price_data" : { "retail_price" : "17.95", "online_price" : "17.95", "our_price" : "17.95", "club_price" : "17.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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