1. A method for processing reference feature vectors comprising the steps of: obtaining reference feature vectors defining a plurality of references; assigning said references to a plurality of classes, each of said classes containing a plurality of references; and forming a plurality of sets of clusters, each said set of clusters being uniquely associated with an associated class, such that each said set has associated therewith all of said reference feature vectors for its associated class, wherein the number of said clusters is significantly less than the number of said reference feature vectors, and wherein each cluster of said set of clusters has associated therewith any number of regions selected from the group of regions consisting of: a possibility region, such that all said reference feature vectors of said associated class of a selected set of clusters are contained in at least one of said possibility region contains relatively few reference feature vectors not belonging to said associated class; and a certainty region in which is contained a plurality of said reference feature vectors which all belong to said associated class of said cluster and which do not contain reference feature vectors which are not of said selected class.
1. A method for processing reference feature vectors comprising the steps of: obtaining reference feature vectors defining a plurality of references; assigning said references to a plurality of classes, each of said classes containing a plurality of references; and forming a plurality of sets of clusters, each said set of clusters being uniquely associated with an associated class, such that each said set has associated therewith all of said reference feature vectors for its associated class, wherein the number of said clusters is significantly less than the number of said reference feature vectors, and wherein each cluster of said set of clusters has associated therewith any number of regions selected from the group of regions consisting of: a possibility region, such that all said reference feature vectors of said associated class of a selected set of clusters are contained in at least one of said possibility region contains relatively few reference feature vectors not belonging to said associated class; and a certainty region in which is contained a plurality of said reference feature vectors which all belong to said associated class of said cluster and which do not contain reference feature vectors which are not of said selected class. 8. The method of claim 1 wherein said step of forming a plurality of sets of clusters includes the step of forming a plurality of sets of certainty regions which comprises the steps of: selecting one of said classes; and selecting a number of said reference feature vectors belonging to said selected class such that each reference feature vector of said number of reference feature vectors forms the center of one certainty region of a set of certainty regions associated with said selected class and such that substantially all reference feature vectors of said selected class are contained in a certainty region for said selected class.