The cascade-correlation learning architecture
Advances in neural information processing systems 2
A Cascade Network Algorithm Employing Progressive RPROP
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Neural network processing for multiset data
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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This paper presents new experimental results on the variadic neural network (VNN) [McGregor, S. (2007). Neural network processing for multiset data. In Proceedings: Vol. 4668. Artificial neural networks - ICANN 2007, 17th international conference (pp. 460-470). Springer]. The inputs to a variadic network are an arbitrary-length list of n-tuples of real numbers, where n is fixed, and the function computed by the network is unaffected by permutation of the inputs. This paper describes improvements in the training algorithm for the variadic perceptron, based on a constructive cascade topology, and performance of the improved networks on geometric problems inspired by vector graphics. Further development may allow practical application of these or similar networks to vector graphics processing and statistical analysis.