Cascading classifiers
We propose a multistage recognition method built as a cascade of a linear parametric model and a -nearest neighbor (-NN) nonparametric classifier. The linear model learns a “rule” and the -NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and...