【摘要】 传统的垃圾短信过滤方案,以垃圾短信中出现的敏感词作为判断的依据,却忽略了正常短信中出现的词对分类的贡献,并且由于短信用语的灵活性,特征提取难度较大。提出了一种基于svm算法对垃圾短信进行监控和过滤的方案,该方案根据短信内容、短信长度等特征,对短信文本进行向量空间的表示。通过机器学习的方式,对垃圾短信进行判断,过滤。相比传统方法而言,本系统在过滤准确度和效率两方面均获得大幅度提升。更多还原

【Abstract】 The traditional scheme to filter spam messages is generally based on the occurrence of sensitive words in them,but ignores the contribution made to the classification by words appearing in normal messages.And the flexibility of message phrases renders feature extraction rather difficult.This text presents a scheme to superintend and filter spam messages on the basis of SVM algorithm.This scheme expresses the text in way of vector space with reference to features like the content and length of messages.And it uses the method of machine learning to judge and filter spam messages.Compared with traditional schemes,this system performs better in terms of filtering efficiency and accuracy.更多还原