Weight Adjustment Technique in the Deep Web Data Source Classification Applied Research

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The Web database's classification is the key step which integrates with the Web database classification and retrieves. The traditional search engine is unable to correct search for the magnanimous information in Deep Web hides. This article has proposed one kind of classification based on machine learning's web database. The experiment has indicated that after this taxonomic approach undergoes few sample training, it can achieve the very good classified effect, and along with training sample's increase, this classifier's performance maintains stable and the rate of accuracy and the recalling rate fluctuate in the very small scope.

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Edited by:

Zhengyi Jiang, Yugui Li, Xiaoping Zhang, Jianmei Wang and Wenquan Sun

Pages:

2920-2923

Citation:

J. Q. Dong, "Weight Adjustment Technique in the Deep Web Data Source Classification Applied Research", Applied Mechanics and Materials, Vols. 220-223, pp. 2920-2923, 2012

Online since:

November 2012

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$38.00

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