摘要：规则学习算法通过学习样本产生规则集，如伺判断规则集的好坏？
Absrtact: the rule extraction algorithm produces the rule set by learning examples. How to evaluate the rule set?
在对归纳学习理论深入研究的基础上，将规则学习算法应用到入侵检测建模中。
Based on the in-deep research on inductive learning theory, a rule learning algorithm is applied in building the intrusion detection model.
最后根据改进的关联规则学习算法的核心思想，对网络安全审计模型进行了设计，并给予实现。
Finally, basing on the core idea of improved association rule learning algorithms, this thesis designs and achieves simple models of network security audit.
实验结果表明，改进的关联规则学习算法在网络安全审计的自适应能力上有较好表现，取得了预期效果。
The experiments show that the improved learning algorithm of association rules in network security audits have better performance of automatic adaptive capacity, achieving the expected effect.
实践表明，规则学习算法用于韵律结构预测达到了90％以上的正确率，优于目前其他方法的结果，是一种行之有效的办法。
The experiments show that the rule-learning approach can achieve a better accuracy rate of 90% than the others. Thus it is justified as an effective way to prosodic structure prediction.
实验结果表明：现有算法以16%的有效扩展规则覆盖了93%的标注正例，并使预期精度从51%提高到81%，显示了这套规则学习和评价方法的有效性。
Test results indicate that the algorithm can acquire about 16% of the useful expanded rules to cover 93% of the annotated positive examples and can improve the expected accuracy from 51% to 81%.
实验结果表明：现有算法以16%的有效扩展规则覆盖了93%的标注正例，并使预期精度从51%提高到81%，显示了这套规则学习和评价方法的有效性。
Test results indicate that the algorithm can acquire about 16% of the useful expanded rules to cover 93% of the annotated positive examples and can improve the expected accuracy from 51% to 81%.
应用推荐