通过对68个常用英文单词的测试，验证了该方法的平均识别正确率达到89.6%。
Experimental results on a test set containing 68 most frequently used English words showed that on average, the approach can achieve an accuracy rate of 89.6%.
作为输入的FTIR特征谱峰不同时，则网络的平均分类识别正确率也不同。
The mean accurate rate of recognition of the LVQ neural network would be different, as the input vectors comtained different kind of FTIR characteristic frequencies.
实验结果显示，主题识别平均正确率为79%文本与主题概念的相关度计算的正确率达到62%。
Experiment results show that the average accuracy of topic identification reaches79%, and relevancy computation reaches62%by this approach.
实验结果显示，主题识别平均正确率为79%。
Experiment results show that the average accuracy of topic identification reaches 79 % by this approach.
实验结果显示，主题识别平均正确率为79%。
Experiment results show that the average accuracy of topic identification reaches 79 % by this approach.
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