Adaptive clustering neural net for piecewise nonlinear discriminant surfaces
David P. Casasent, Etienne Barnard · 1990
A three-layer adaptive clustering neural net is described for distortion-invariant multiclass object recognition in difficult problems requiring piecewise nonlinear discriminant surfaces. The number of hidden-layer neurons is determined by an organized procedure (several neurons are used per class as prototypes of each class). These are chosen by clustering techniques. The vector description of each prototype in the multidimensional input feature space specifies a set of linear discriminant functions that are the initial input to the hidden-layer weights used. These weights are then refined by a neural net algorithm using conjugate gradient techniques to produce the final weights. A neural net (NN) that marries pattern-recognition and NN techniques is thus obtained. Various multiclass distortion-invariant classification results are presented