Learning error-correcting output codes from data

A polychotomizer which assigns the input to one of K⩾3 classes is constructed using a set of dichotomizers which assign the input to one of two classes. Defining classes in terms of the dichotomizers is the binary decomposition matrix of size K×L where each of the K⩾3 classes is written as error-correcting output codes (ECOC), i.e., an array of the responses of binary decisions made by L dichotomizers. We use linear dichotomizers and by combining them suitably, we build nonlinear polychotomizers, thereby reducing complex decisions into a group of simpler decisions. We propose a method to learn the error-correcting codes from data based on soft weight sharing which forces parameters to take one of a set (here two: -1/+1) values. Simulation results on eight datasets indicate that compared with a linear one-per-class polychotomizer and ECOC proper, these methods generate more accurate classifiers, using less dichotomizers than pairwise classifiers