Bootstrapping for efficient handwritten digit recognition

摘要

In this paper we present two algorithms for selecting prototypes from the given training data set. Here, we employ the bootstrap technique to preprocess the data. We compare the proposed algorithms with the condensed nearest-neighbor algorithm which is order dependent and a genetic-algorithm-based prototype selection scheme which is order independent. Algorithms proposed in this paper are found to be better than the condensed nearest neighbor and prototype selection methods in terms of classification accuracy.