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Web Classification Using Deep Belief Networks

机译:使用深层信任网络进行Web分类

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摘要

In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks to web data and evaluate the algorithm on various classification experiments by comparing its performance with that of the SVM classification algorithm. In addition, the experiments show good performance of the deep belief networks for various classification tasks.
机译:近年来,深度学习方法作为一种从未标记数据构建层次表示的方法,引起了人们的极大兴趣。这些深度学习方法已应用于图像识别,语音识别和文本处理。但是,据我们所知,尚未对Web数据深入研究深度学习方法。在本文中,我们将深度置信网络应用于Web数据,并通过将其性能与SVM分类算法的性能进行比较,对算法进行各种分类实验进行评估。此外,实验显示了深度信念网络对于各种分类任务的良好性能。

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