A large number of experimental results have shown that this method can get good performance on most of the data sets, and the average error rate is better than the genetic algorithm that was implemented on Weka platform.
大量的实验同时表明这种方法在绝大部分数据集上都有良好的性能,并且其平均错误率优于Weka平台上已实现的那种基于遗传算法的属性选择方法。
参考来源 - 数据挖掘中属性选择算法的分析与研究·2,447,543篇论文数据，部分数据来源于NoteExpress
实验结果表明前馈后向传播网络的性能最好，与基准模型比较平均错误率下降54.4%。
Experiment results show that feed-forward backpropagation network achieves the best performance, which reduces average error rate by54.4%.
研究结果表明，水印检测错误率主要取决于水印的平均能量和水印容量，而且随着水印容量的提高而增加。
Reseatch result shows that watermark detection error is mainly influenced by the watermark mean energy and watermarking capacity. The detection error rises with the increase of watermarking capacity.
拒识的平均正识率为86.33%，拒识后平均候选个数降为3.46(未进行拒识前是10名候选)，总的拒识错误率为0.27%。
The average recognition accuracy is 86.33% with 3.46 candidate number (the number was 10 before rejection) on an average, and the total error rejection rate is 0.27%.
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