Images, frames, and connectionist hierarchies.
The representation of hierarchically structured knowledge in systems using distributed patterns of activity is an abiding concern for the connectionist solution of cognitively rich problems. Here, we use statistical unsupervised learning to consider semantic aspects of structured knowledge representation. We meld unsupervised learning notions formulated for multilinear models with tensor product ideas for representing rich information. We apply the model to images of faces.
Dayan
Peter
P
Gatsby Computational Neuroscience Unit, University College London, London WC1N@3AR. dayan@gatsby.ucl.ac.uk
D002965:Classification / Q000379:methods*; D005103:Expert Systems; D005145:Face; D006801:Humans; D007092:Imagination; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D011582:Psychological Theory