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• W1963142789 abstract "We present a framework in which self-organizing systems can be used to perform change of representation on knowledge discovery problems and to learn from very large databases. Clustering using self-organizing maps is applied to produce multiple, intermediate training targets that are used to define a new supervised learning and mixture estimation problem. The input data is partitioned using a state space search over subdivisions of attributes, to which self-organizing maps are applied to the input data as restricted to a subset of input attributes. This approach yields the variance-reducing benefits of techniques such as stacked generalization, but uses self-organizing systems to discover factorial (modular) structure among abstract learning targets. This research demonstrates the feasibility of applying such structure in very large databases to build a mixture of ANNs for data mining and KDD." @default.
• W1963142789 created "2016-06-24" @default.
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• W1963142789 date "2003-01-22" @default.
• W1963142789 modified "2023-09-23" @default.
• W1963142789 title "Self-organizing systems for knowledge discovery in large databases" @default.
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• W1963142789 doi "https://doi.org/10.1109/ijcnn.1999.833461" @default.
• W1963142789 hasPublicationYear "2003" @default.
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