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Kernel method is an effective and popular trick in machine learning. In this paper, by introducing it into conventional auto-associative memory models (AMs), we construct a unified framework of kernel auto-associative memory models (KAMs), which makes the existing exponential and polynomial AMs become its special cases. Further, in order to reduce KAM's connect complexity, inspired by "small-world network" recently described by Watts and Strogatz, we propose another unified framework ofdoi:10.1016/j.neucom.2005.02.001 fatcat:kg7eoqaj2fhljo4oebjfkyedka