1. A computer-implemented method utilizing a neural network having a raw input layer, for fitting a model of similarity to a set of similarity judgments familiar to a human user for application in software tools for assisting said human user in performing tasks requiring similarity judgments, whereby said tasks may include any of classification and clustering, comprising the steps of: (a) inputting a set of judgments, one at a time, into said raw input layer of said neural network, wherein each of set of judgments comprises a triple of objects &lt;S,G,B&gt;, where S is more similar to G than S is to B, and with each respective object being represented by a vector of features present in each said respective object; (b) coupling outputs of said raw input layer of said neural network to respective inputs of an input layer of a duplicated neural network where said duplicated neural network comprises two identical copies of a simpler network, with first and second sets of link weights being used for respective ones of said two identical copies, said first and second sets of link weights being identical, and with input couplings so arranged that one of said identical copies computes the similarity of S to B, and the other of said identical copies computes the similarity of S to G, said simpler network comprising a desired functional form of said model of similarity; (c) coupling an output layer of said duplicated neural network to a final output node which computes a result indicative of the difference between two similarity values previously computed by said two identical copies, applies an activation function to said result, and compares a resulting value to a predetermined threshold to derive an error value; and (d) deriving optimal link weights for said model of similarity by backpropagating said error value through said neural network.