对两种阈值和偏置计算方法的仿真实验结果表明，在错分率降可接受的范围内，二者均使用较少的弱分类器便可获得高识别率的强分类器。
Simulation experiments show when the error rate is in an acceptable range, the algorithms using fewer weak classifiers will be able to guarantee the strong classifier to maintain a high correct rate.
Bayesian网络或神经网络等技术使用表达能力非常强的模型，力求生成无偏向的分类器来“描述”文档集。
Techniques such as Bayesian networks or neural networks use highly expressive models, which try to produce a non-biased classifier in order to "describe" a corpus of documents.
支持向量机分类器结构简单、可获得全局最优、泛化能力强。
SVM has good characteristics of simple structure, global optimum and strong generalization ability.
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