Understanding classification using nearest neighbors
In a single sentence, nearest neighbor classifiers are defined by their characteristic of classifying unlabeled examples by assigning them the class of the most similar labeled examples. Despite the simplicity of this idea, nearest neighbor methods are extremely powerful. They have been used successfully for:
Computer vision applications, including optical character recognition and facial recognition in both still images and video
Predicting whether a person enjoys a movie which he/she has been recommended (as in the Netflix challenge)
Identifying patterns in genetic data, for use in detecting specific proteins or diseases
In general, nearest neighbor classifiers are well-suited for classification tasks where relationships among the features and the target classes are numerous, complicated, or otherwise extremely difficult to understand, yet the items of similar class type tend to be fairly homogeneous. Another way of putting it would be to...
