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    • 7. 发明申请
    • Trees of classifiers for detecting email spam
    • 用于检测电子邮件垃圾邮件的分类树
    • US20070038705A1
    • 2007-02-15
    • US11193691
    • 2005-07-29
    • David ChickeringGeoffrey HultenRobert RounthwaiteChristopher MeekDavid HeckermanJoshua Goodman
    • David ChickeringGeoffrey HultenRobert RounthwaiteChristopher MeekDavid HeckermanJoshua Goodman
    • G06F15/16
    • H04L51/12
    • Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.
    • 利用分类器模型填充的决策树利用电子邮件的每个功能使用单独的电子邮件分类器来提供增强的垃圾邮件检测。 这通过定制每个分类器模型提供了更高的垃圾邮件检测的概率,以便于在逐个特征的基础上更准确地确定垃圾邮件。 分类器可以基于诸如逻辑回归模型和/或支持向量机(SVM)等线性模型来构建。 分类器也可以基于决策树构建。 基于决策树的内部和/或外部节点的“复合特征”也可以用于提供线性分类器模型。 垃圾邮件检测结果的平滑可以通过使用来自决策树内的其他节点的分类器模型来实现,如果训练数据是稀疏的。 这形成了可能没有接收到大量训练数据的决策树的分支的基本模型。