Java implementation of the K-nearest neighbor classifier
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification. The input consists of the k closest (depends of the considered distance) training examples in the feature space. The output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
Sipmply execute the main method in the Launcher class and play with the parameters.
You may have to download dependencies using mvn clean install
