ADAPTIVE NEAREST NEIGHBOR PATTERN-CLASSIFICATION

We describe a variant of nearest neighbor pattern classification (NN) [1] and supervised learning by learning vector quantization (LVQ) [2], [3]. The decision surface mapping method, which we call DSM, is a fast supervised learning algorithm, and is a member of the LVQ family of algorithms. A relatively small number of prototypes are selected from a training set of correctly classified samples. The training set is then used to adapt these prototypes to map the decision surface separating the classes. This algorithm is compared with NN pattern classification, learning vector quantization (LVQ1) [2], and a two-layer perception trained by error backpropagation [4]. When the class boundaries are sharply defined (i.e., no classification error in the training set) the DSM algorithm outperforms these methods with respect to error rates, learning rates, and the number of prototypes required to describe class boundaries.