Adaptive graphical pattern recognition: The joint role of structure, learning

In this paper we introduced methodological and practical issues on adaptive graphical pattern recognition, a new approach to process patterns for which neither a purely symbolic nor a purely sub-symbolic representation seems to be adequate. Adaptive graphical pattern recognition is based on appropriate graphical representations of patterns which are subsequently processed by recursive neural networks, equipped with connectionist-based learning algorithms. The preliminary ideas and results given in [1] are extended and some properties, like the capabilities of incorporating scale and rotation invariance and dealing with noise, are emphasized and related to competing approaches. It turns out that adaptive graphical pattern recognition bridges nicely the gap between traditional connectionist models and syntactic pattern recognition and appears to be a new challenging approach to many pattern recognition problems.