Further results in multiset processing with neural networks.
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.
McGregor
Simon
S
Centre for Computational Neuroscience and Robotics (CCNR), University of Sussex, United Kingdom. sm66@sussex.ac.uk
D000818:Animals; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010465:Perception; D012815:Signal Processing, Computer-Assisted