AN ASYMMETRIC ADAPTIVE CLASSIFICATION METHOD

摘要

There should be different requirements for False Reject rate and False Accept rate innclassification applications, and classifier learning should use an asymmetric factor tonbalance between False Reject rate and False Accept rate. A novel AdaBoost algorithmnwas developed with the asymmetric weight. Moreover we provide the theoretical analysisnof its performance and derive the upper bound of the classification error