The threshold effect of a nonlinear learning algorithm for pattern recognition

Abstract This paper deals with a nonlinear learning algorithm characterized by a threshold effect. The algorithm is derived in relation to a learning process in pattern recognition where steps must be taken to minimize the effect of spurious samples carrying unreliable information. With the assumption of a multivariate normal density for each pattern population, the nonlinear algorithm reduces to discarding the spurious samples with a threshold element and in the meantime performing an ordinary linear algorithm for the remaining samples. Optimal properties of this algorithm are studied and a criterion for selecting a proper threshold is discussed. A computer-simulated experiment in character recognition is presented to illustrate a possible application for the nonlinear algorithm. Results of this experiment have shown significant improvement over an ordinary linear algorithm in handling character samples with various imperfections due to the poor quality in writing or printing encountered in practice.