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A computational intelligent system that models the human cognitive abilities may promise significant performance in problem learning because human is effective in learning and problem solving. Functionally modelling the human cognitive abilities not only avoids the details of the underlying neural mechanisms performing the tasks, but also reduces the complexity of the system. The complementary learning mechanism is responsible for human pattern recognition, i.e. human attends to positive and negative samples when making decision. Furthermore, human concept learning is organized in a hierarchical fashion. Such hierarchical organization allows the divide-and-conquer approach to the problem. Thus, integrating the functional models of hierarchical organization and complementary learning can potentially improve the performance in pattern recognition. Hierarchical complementary learning exhibits many of the desirable features of pattern recognition. It is further supported by the experimental results that verify the rationale of the integration and that the hierarchical complementary learning system is a promising pattern recognition tool.
cognitive learning, complementary learning, decision support, hierarchical model, fuzzy neural network

T. Z. Tan, C. Quek and G. S. Ng, "A Novel Biologically and Psychologically Inspired Fuzzy Decision Support System: Hierarchical Complementary Learning," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 5, no. , pp. 67-79, 2007.
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