Classifier Design for Population and Sensor Drift
The basic assumption in classifier design is that the distribution from which the design sample is selected is the same as the distribution from which future objects will arise: i.e., that the training set is representative of the operating conditions. In many applications, this assumption is not valid. In this paper, we discuss sources of variation and possible approaches to handling it. We then focus on a problem in radar target recognition in which the operating sensor differs from the sensor used to gather the training data. For situations where the physical and processing models for the sensors are known, a solution based on Bayesian image restoration is proposed.
Keywordsclassification generalisation sensor drift population drift Bayesian inference target recognition
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