基于全内容的深度神经网络网页分类方法

Suleyman Suleymanzade, F. Abdullayeva
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引用次数: 3

摘要

网页分类过程的质量对信息检索系统有着巨大的影响。在本文中,我们提出将文本和图像数据分类器的结果结合起来,以获得网页的准确表示。为了获取和分析数据,我们创建了包含数据挖掘器、文本分类器和聚合器的复杂分类器系统。通过深度学习模型实现了图像和文本数据的分类过程。为了在网页上表示公共视图,我们提出了三种聚合技术,将来自分类器的数据组合在一起。
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Full Content-based Web Page Classification Methods by using Deep Neural Networks
The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers.
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