Authorship verification as a one-class classification problem
Authorship verification as a one-class classification problem
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DOI：
10.1145/1015330.1015448
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
Moshe Koppel;Jonathan Schler
中科院分区：
文献类型：
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作者：
Moshe Koppel;Jonathan Schler
在作者身份验证问题中，我们被给出了单个作者的写作例子，并被要求确定给定的长文本是否由该作者撰写。我们提出了一种新的基于学习的方法来引用两个示例集之间的“差异深度”，并提供了证据表明该方法解决了作者身份验证问题，具有很高的准确率。基本的想法是测试学习模型的精确度的降级率，因为最佳特征被迭代地从学习过程中丢弃。
In the authorship verification problem, we are given examples of the writing of a single author and are asked to determine if given long texts were or were not written by this author. We present a new learning-based method for adducing the "depth of difference" between two example sets and offer evidence that this method solves the authorship verification problem with very high accuracy. The underlying idea is to test the rate of degradation of the accuracy of learned models as the best features are iteratively dropped from the learning process.