Representation and training of vector graphics with NRAAM networks

Recursive auto-associative memory networks (RAAM) are neural networks that can be trained to represent structured information. After training, this information can be retrieved following its inner structure. By now, RAAM networks were applied only to syntactical expressions like parse trees of natural language sentences or logical terms. In this paper it is shown how they can be used for representing vector graphics that are given as a tree. For this purpose we developed name RAAM networks (NRAAM) which are more suitable for the training of complex information than normal RAAMs.