A constructive algorithm that converges for real-valued input patterns.
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.
Burgess
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Department of Anatomy, University College, London, U.K.
D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer