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In the field of image recognition, a high-dimensional feature vector is often used to construct a classifier. This presents a problem, however, since using a large number of features can slow down training and degrade model readability. To alleviate this problem, sequential backward selection (SBS) has come to be used as a method for selecting an effective number of features for classification. However, as a type of wrapper method, SBS iteratively constructs and evaluates classifiers whendoi:10.1109/fcv.2015.7103746 dblp:conf/fcv/MurataMYYF15 fatcat:sn7q3s22kraobd7i5avkas4bgu