Distance-based Classification of Structures within a Connec- tionist Framework

We present a novel self-organizing classification network for structured objects. The system consists of two layers, an upper layer which serves as a classifier and a lower layer as a feature extractor. The classifier is an inhibitory winner-take-all network where each unit corresponds to a unique class while the lower layer consists of Hopfield-style networks solving the problem of finding maximum common parts of the input structure and the particular prototypes. The self-organizing architecture is able to outperform traditional classification models using a maximum selector as a classifier. The improvements arise from deactivating networks in the lower layer which indicate large dissimilarities between the input structure and the corresponding prototypes.