A Constructive Algorithm that Converges for Real-Valued Input Patterns

  title={A Constructive Algorithm that Converges for Real-Valued Input Patterns},
  author={Neil Burgess},
  journal={International journal of neural systems},
  volume={5 1},
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… CONTINUE READING
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