EFFICIENT ADAPTIVE LEARNING FOR CLASSIFICATION TASKS WITH BINARY UNITS

This article presents a new incremental learning algorithm for classif ication tasks, called NetLines, which is well adapted for both binary and real-valued input patterns. It generates small, compact feedforwar d neural networks with one hidden layer of binary units and binary out put units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued input pat terns. An implementation for problems with more than two classes, vali d for any binary classifier, is proposed. The generalization error and the size of the resulting networks are compared to the best published results on well-known classification benchmarks. Early stopping is sh own to decrease overfitting, without improving the generalization perf ormance.