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Among the topics are probability densities, the pattern recognition problem, the nearest neighbor rule, infinite VC (Vapnik-Chervonenkis) dimension, the function estimation problem, and support vector machines.
The idea is similar to the Reduced Nearest Neighbor rule (RNN), but in this case the algorithm consider if the deleted instance could be classified correctly, while the RNN takes into account if the other instances classification could be damaged by the elimination of those instances.
Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting.