{"product_id":"an-investigation-of-the-effects-of-correlation-and-autocorrelation-in-classifier-fusion-with-non-declarations-1288307268","title":"An Investigation of the Effects of Correlation and Autocorrelation in Classifier Fusion with Non-Declarations","description":"\u003cp\u003e\u003cstrong\u003eISBN:\u003c\/strong\u003e 1288307268\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor:\u003c\/strong\u003e Mindrup, Frank M\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e New\u003c\/p\u003e\u003cp\u003eAir 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.\u003c\/p\u003e","brand":"Mia Karts","offers":[{"title":"Default Title","offer_id":51964318482720,"sku":"NEW1288307268","price":21.54,"currency_code":"USD","in_stock":false}],"url":"https:\/\/miakarts.com\/products\/an-investigation-of-the-effects-of-correlation-and-autocorrelation-in-classifier-fusion-with-non-declarations-1288307268","provider":"Miakarts Books","version":"1.0","type":"link"}