基于有监督人工神经网络算法的Web垃圾邮件分类
Web spam classification using supervised artificial neural network algorithms
摘要：
由于垃圾邮件发送者所采用的技术的快速增长，需要更有效、通用和高度自适应的分类器。基于神经网络的技术具有很强的自适应能力和泛化能力。据我们所知，在这一领域中使用神经网络的工作很少。我们提出本文来填补这一空白。本文通过创建分类器来评估人工神经网络的三种监督学习算法的性能，以解决最新Web垃圾邮件模式分类的复杂问题。这些算法是共轭梯度算法，弹性反向传播学习和Levenberg-Marquardt算法。
英文摘要：
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
