摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者在印度班加鲁的新闻报道，研究表明：“现在，各种语言时代的所有记录都可以通过先进的结构获得。为了简单地恢复这些数字化记录，这些报告应该按照内容分类。”摘要:新闻记者引用了美国总统大学的一篇研究报告:“文本分类是文本挖掘的一个领域,它有助于克服这一难题。文本分类是对记录进行分类的一个示范。本文调查了外语文本分类的工作。”摘要:语言种类繁多,信息种类繁多,语言种类繁多决策树和支持向量机在文本分类任务中表现得更好。自动文档分类技术在我们日常生活中非常有用，可以根据文本内容找出语言类型和不同部门的书籍。我们在这里使用了不同的外国和地区局域网来分类，如泰米尔语、泰卢固语、卡纳达语、孟加拉语、英语、西班牙语、法语、俄语和德语。在所有情况下，我们使用一个与所有支持向量机进行多特征化，并通过三折交叉验证，看到支持向量机输出执行不同的分类器。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Bengaluru, India, by NewsRx journalists, research stated, "Nowadays, all the records in various langu ages are accessible with their advanced structures. For simple recovery of these digitized records, these reports should be ordered into a class as indicated by their content." The news reporters obtained a quote from the research from Presidency University , "Text Categorization is an area of Text Mining which helps to overcome this ch allenge. Text Classification is a demonstration of allotting classes to records. This paper investigates Text Classification works done in foreign Languages, re gional languages and a list of books' content. Messages available in different l anguages force the difficulties of NLP approaches. This study shows that supervi sed ML algorithms such as Logistic regression, Naive Bayes classifier, k-Nearest -Neighbor classifier, Decision Tree and SVMs performed better for Text Classific ation tasks. The automated document classification technique is useful in our da y-to-day life to find out the type of language and different department books ba sed on their text content. We have been using different foreign and regional lan guages here to classify such as Tamil, Telugu, Kannada, Bengali, English, Spanis h, French, Russian and German. Here, we utilize one versus all SVMs for multi-ch aracterization with 3-crease Cross Validation in all cases and see that SVMs out perform different classifiers."
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