Domain-Relied QSOFM and its Application in Deep Web Topic Classification

Liang Zhang; Yu Liang Lu; Jin Hong Liu

doi:10.4028/www.scientific.net/AMR.255-260.2067

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260Domain-Relied QSOFM and its Application in Deep...

Domain-Relied QSOFM and its Application in Deep Web Topic Classification

Abstract:

To solve the problem of Deep Web data sources topic classification, this paper proposed a quantum self-organization feature mapping network model(DR-QSOFM)with a classification algorithm. DR-QSOFM combines quantum computation and traditional SOFM, and relies the feature vectors and target vectors incoordinately in different phases of training, making a more centralized distribution of winner neurons in competitive layer and more obvious boundaries among clusters. Some experiments are designed and done on the expanded TEL-8 dataset to test the validity of DR-QSOFM.

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Advances in Civil Engineering, CEBM 2011

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Periodical:

Advanced Materials Research (Volumes 255-260)

Pages:

2067-2071

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.255-260.2067

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Online since:

May 2011

Authors:

Liang Zhang, Yu Liang Lu, Jin Hong Liu

Keywords:

Domain Relied, Quantum Self-Organization Feature Mapping, Topic Classification

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