Self-organizing neural network for classifying pattern signatures with `a posteriori` conditional class probability

Claim 1

A self-organizing neural network for classifying an N-feature pattern signature with a posteriori conditional class probability that the pattern signature belongs to a selecting class from a plurality of classes, comprising: a first plurality of processing layers for storing each of a first plurality of training vectors as non-overlapping clusters, each of said first plurality of training vectors having N-feature coordinates and a class coordinate, each of said non-overlapping clusters having a center and a radius defined by a vigilance parameter, wherein the center of each of said non-overlapping clusters is a reference vector, wherein the reference vector associated with each of said non-overlapping clusters is successively indexed and is equal to a corresponding one of said first plurality of training vectors, said first plurality of processing layers updating one cluster from said non-overlapping clusters and storing said first plurality of training vectors when one of a second plurality of training vectors has corresponding N-feature coordinates bounded by the radius of said one cluster, wherein N-feature coordinates of a cluster's reference vector are recursively combined with N-feature coordinates of said one of said second plurality of training vectors having corresponding N-feature coordinates bounded by the radius of said one cluster, wherein a plurality of trained clusters are generated using said first and second plurality of training vectors, said first plurality of processing layers determining i) a count of training vectors associated with the selected class that are bounded within each of said plurality of trained clusters, and ii) a total count of training vectors bounded within each of said plurality of trained clusters, wherein each of said plurality of trained clusters has a reference vector that defines a fractional probability associated with the selected class based upon a ratio of i) said count of training vectors associated with the selected class to ii) said total count of training vectors bounded by a corresponding one of said plurality of trained clusters; and means for processing an input vector defining the pattern signature to be classified, the input vector having N-feature coordinates associated with an unknown class, said processing means for selecting one of reference vectors associated with said plurality of training vectors that minimizes differences with the N-feature coordinates of the input vector, wherein a fractional probability of the selected one of the reference vectors is the a posteriori conditional class probability that the input vector belongs to the selected class.

Claim 12

A method, utilizing a self-organizing neural network, for classifying an N-feature pattern signature with a posteriori conditional class probability that the pattern signature belongs to a selecting class from a plurality of classes, comprising the steps of: storing each of a first plurality of training vectors as non-overlapping clusters in a first plurality of processing layers, each of said first plurality of training vectors having N-feature coordinates and a class coordinate, each of said non-overlapping clusters having a center and a radius defined by a vigilance parameter, wherein the center of each of said non-overlapping clusters is a reference vector, wherein the reference vector associated with each of said non-overlapping clusters is successively indexed and is equal to a corresponding one of said first plurality of training vectors; updating one cluster from said non-overlapping clusters and storing said first plurality of training vectors when one of a second plurality of training vectors has corresponding N-feature coordinates bounded by the radius of said one cluster, wherein said step of updating is defined as N-feature coordinates of a cluster's reference vector being recursively combined with N-feature coordinates of said one of second plurality of training vectors having corresponding N-feature coordinates bounded by the radius of said one clusters, wherein a plurality of trained clusters are generated using said first and second plurality of training vectors; determining i) a count of training vectors associated with the selected class that are bounded within each of said plurality of trained clusters, and ii) a total count of training vectors bounded within each of said plurality of trained clusters, wherein each of said plurality of trained clusters has a reference vector that defines a fractional probability associated with the selected class based upon a ratio of i) said count of training vectors associated with the selected class to ii) said total count of training vectors bounded by a corresponding one of said plurality of trained clusters; determining, using the first plurality of processing layers, a difference between N-feature coordinates of an input vector and each of the reference vectors associated with said plurality of trained clusters; and selecting, using a second plurality of processing layers, one of reference vectors associated with said plurality of training vectors that minimizes said difference, wherein a fractional probability of the selected one of the reference vectors is the a posteriori conditional class probability that the input vector belongs to the selected class.

Claim 13

A method, utilizing a self-organizing neural network, for classifying an N-feature pattern signature with a posteriori conditional class probability that the pattern signature belongs to a selecting class from a plurality of classes, comprising the steps of: storing each of a first plurality of training vectors as non-overlapping clusters in a first plurality of processing layers, each of said first plurality of training vectors having N-feature coordinates and a class coordinate, each of said non-overlapping clusters having a center and a radius defined by a vigilance parameter, wherein the center of each of said non-overlapping clusters is a reference vector, wherein the reference vector associated with each of said non-overlapping clusters is successively indexed and is equal to a corresponding one of said first plurality of training vectors; updating one cluster from said non-overlapping clusters and storing said first plurality of training vectors when one of a second plurality of training vectors has corresponding N-feature coordinates bounded by the radius of said one cluster, wherein said step of updating is defined as N-feature coordinates of a cluster's reference vector being recursively combined with N-feature coordinates of said one of second plurality of training vectors having corresponding N-feature coordinates bounded by the radius of said one clusters, wherein a plurality of trained clusters are generated using said first and second plurality of training vectors; determining i) a count of training vectors associated with the selected class that are bounded within each of said plurality of trained clusters, and ii) a total count of training vectors bounded within each of said plurality of trained clusters, wherein each of said plurality of trained clusters has a reference vector that defines a fractional probability associated with the selected class based upon a ratio of i) said count of training vectors associated with the selected class to ii) said total count of training vectors bounded by a corresponding one of said plurality of trained clusters; determining, using the first plurality of processing layers, a difference between N-feature coordinates of an input vector and each of the reference vectors associated with said plurality of trained clusters; and selecting, using a second plurality of processing layers, one of reference vectors associated with said plurality of training vectors that minimizes said difference, wherein a fractional probability of the selected one of the reference vectors is the a posteriori conditional class probability that the input vector belongs to the selected class.