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A constructive algorithm that converges for real-valued input patterns.

N Burgess
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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.

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BibTeXRIS

N Burgess. 1994. A constructive algorithm that converges for real-valued input patterns.. https://doi.org/10.1142/s0129065794000074

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