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While many techniques exist to classify data possessing straightforward characteristics, they tend to fail when dealing with the ldquocurse of dimensionalityrdquo. This condition, in which the ratio of features to samples is very large, is prevalent in many complex, voluminous biomedical datasets acquired using current spectroscopic modalities. We present a novel classification method using an adaptive...
The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes' classifier based on the estimated a posteriori probabilities from the k-nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assumption. This assumption, however, becomes problematic...
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