THE HOTELLING?S TWO-SAMPLE T2 ALGORITHM: STATISTICAL CLASSIFIER: CASE STUDY OF THE HOTELLING?S TWO-SAMPLE T2 ALGORITHM IN THE PRESENCE OF NOISE - Softcover

Saparkhojayev, Nurbek

Softcover

ISBN 10: 3838326547 ISBN 13: 9783838326542

Publisher: LAP Lambert Academic Publishing, 2010

Synopsis

Matching algorithms or classifiers determine if a previously enrolled instance matches an observed instance based on some rules. They return a decision, which consists of three possible answers: match, non-match, and unclassified. A classifier assigns a class label to a sample and then checks the new instance with a sample one. Or, the classifier is trained with example instances so that it learns what class label should be applied to future unknown instances. Classifiers are based on statistical, probabilistic, and decision rules. In applying classifiers, the most important issue is finding the matching rates. Two important rates are the false acceptance rate (FAR) and the false rejection rate (FRR). In this work, we determine the FAR and FRR for the Hotelling?s two-sample T2 algorithm applied to the application of matching electronic fingerprints of radio frequency identification (RFID) tags in the presence of simulated noise. The algorithm is found to be a robust classifier for this application.

Edition details

• Publisher: LAP Lambert Academic Publishing
• Publication date: 2010
• Language: English
• ISBN 10: 3838326547
• ISBN 13: 9783838326542
• Binding: Paperback
• Number of pages: 80