Adaptive Resonance Theory and Diffusion Maps for Clustering Applications in Pattern Analysis
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Abstract
Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.
This paper has been withdrawn.
