A CONSTRUCTIVE ALGORITHM THAT CONVERGES FOR REAL-VALUED INPUT PATTERNS

Abstract

A constructive algorithm is presented which combines the architecture of Cascade Correlation and the training of perceptron-like hidden units with the specific error-correcting roles of Upstart. Convergence to zero errors is proved for any consistent classification of real-valued pattern vectors. Addition of one extra element to each pattern allows hyper-spherical decision regions and enables convergence on real-valued inputs for existing constructive algorithms. Simulations demonstrate robust convergence and economical construction of hidden units in the benchmark “N-bit parity” and “twin spirals” problems.