摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-研究人员详细介绍了人工智能中的新数据。根据NewsRx记者从中国柳州发回的新闻报道，研究表明：“垃圾邮件分类在电子邮件过滤和内容审计系统中越来越重要。”我们的新闻记者从广西科技大学的研究中得到一句话：“尽管SPA M过滤方法不断发展，但垃圾邮件发送者仍在采用新的垃圾邮件检测方法，使我们不堪重负。此外，强大的本文以一个包含5572个垃圾邮件实例的大型垃圾邮件数据集为研究对象,对两种常用的机器学习算法进行了比较分析,并对其进行了仿真。对这两种算法进行了详细描述，包括它们的理论基础和在垃圾邮件检测中的实际实现。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in artificial intelli gence. According to news reporting originating from Liuzhou, People’s Republic o f China, by NewsRx correspondents, research stated, “Spam classification has bec ome more and more significant in email filtering and content auditing systems no wadays.” Our news reporters obtained a quote from the research from Guangxi University of Science and Technology: “Despite the development of many ways for filtering spa m, spammers continue to adopt new methods for spam detection, which has left us overwhelmed with spam. Furthermore, robust, and flexible categorization algorith ms are necessary to keep up with the constant evolution of spam tactics. The bes t method for categorizing and filtering spam now is to use machine learning tech niques. In this study, a large spam dataset containing 5572 email instances is u sed in simulations for the spam classification task. This study comparatively an alyzes two prevalent machine learning algorithms, namely, Random Forest and Naiv e Bayes. A detailed description of both algorithms, including their theoretical foundations and practical implementations in spam detection, is provided.”
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