To the problems higher rate of false retrieval in anomaly detection system due to the uncertainty of intrusion, this paper presents an Anomaly Detection Model Based on Q- Learning Algorithm (QLADM).
针对网络入侵的不确定性导致异常检测系统误报率较高的不足，提出一种基于Q-学习算法的异常检测模型（QLADM）。 该模型把Q-学习、行为意图跟踪和入侵预测结合起来，可获得未知入侵行为的检测和响应。
We can judge the true or false periodicals by way of Internet information retrieval.
我们可以通过网络信息检索法来判断期刊真假。
The results of the experiment show that the algorithm proposed can not only detect those false expansion terms but also improve and enhance the information retrieval performance.
实验结果表明本文算法不仅能发现虚假扩展词，而且还能改善和提高信息检索性能。
The results of the experiment show that the algorithm proposed can not only detect those false expansion terms but also improve and enhance the information retrieval performance.
实验结果表明本文算法不仅能发现虚假扩展词，而且还能改善和提高信息检索性能。
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