A quick and naive Euclidean learner for supervised feature selection
Tony Y. T. Chan · 2003
A model is proposed for learning to classify patterns under the Euclidean setting. Each pattern is represented by a vector in a fixed D-dimensional Euclidean space. Patterns are divided into training and test sets. Eleven experiments were performed. The proposed naive learner is found to be extremely fast and yet the correct classification rates are respectable even when compared with some of the best known rates.