1. A classification method for classifying an input into one of a plurality of possible outputs, comprising the steps of: (a) generating, with a computer processor, a feature vector in a feature space, said feature vector being representative of said input; (b) calculating, with the computer processor, a distance measure in said feature space from said feature vector to the center of each neuron of a neural network in said feature space, said neural network comprising a plurality of neurons, wherein said each neuron is associated with one possible output of said plurality of possible outputs; (c) selecting, with the computer processor, each neuron of said plurality of neurons that encompasses said feature vector in accordance with said distance measure; (d) determining, with the computer processor, a vote for each possible output of said plurality of possible outputs, wherein said vote is a function of the number of said selected neurons that are associated with said each possible output; (e) if one said vote for a possible output is greater than all other said votes for all other possible outputs then classifying, with the computer processor, said input to be said possible output that has the greatest vote; and (f) if one said vote for a possible output is not greater than all other said votes for all other possible outputs then: (f) (1) identifying, with the computer processor, a neuron of said plurality of neurons that has the smallest distance measure of all other said neurons; and (f) (2) classifying, with the computer processor, said input to be the possible output associated with said identified neuron.