Small World Structure Inspired Many to Many Kernel Associative Memory Models and Their Application
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    Abstract:

    Kernel method is an effective and popular trick in machine learning, and small world network is a common phenomenon which exists widely in social fields. In this paper, by introducing them into Hattori et al’s multi-module associative memory for many-to-many associations ((MMA)2), a unified framework of small world structure inspired many-to-many kernel associative memory models (SWSI-M2KAMs) is proposed. The SWSI-M2KAMs not only can store patterns online without more iteration steps, but also extend the range of the processed intelligent information. More importantly, the SWSI-M2KAMs framework can develop more new many-to-many associative memory models by selecting different kernel functions and reduce models’ configuration complexity by using the sparse small world architecture. Finally, computer simulations demonstrate that the constructed models have good performance on many-to-many associative memory.

    Reference
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陈蕾,陈松灿,张道强.小世界体系的多对多核联想记忆模型及其应用.软件学报,2006,17(2):223-231

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History
  • Received:February 28,2005
  • Revised:July 11,2005
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