一个带有React的HackerNews Stories应用程序 使用HackerNews API和React的示例应用程序。 npm install npm start

kNN(k-nearest neighbors algorithm) 此专案以新闻分类进行kNN范例之实作 kNN Introduction: 最近鄰居法（KNN演算法，又譯K-近鄰演算法）是一種用於分類和回歸的無母數統計方法，KNN常用來做資料分類。 KNN是一種監督式學習(Supervised Learning)，監督式學習需透過資料訓練出一個model，但KNN沒有做training的動作。 K為使用者自己定義的常數，KNN就是選擇離自己最近的K的鄰居(Data)，之後觀察哪一種類別(Tag)的鄰居最多就將自己也當成該類別。 Input: 测试文章: 1.使用ETtoday新聞作為訓練集分類。 2.使用Jieba作為分詞，取出Top 100 Words 作為每篇文章的關鍵詞。 3.取出k=3個最近鄰居作為分類依據，此外對最近的第一個鄰居作為加權*2 Output:

spacy德语资源，下载完后在下载路径下进行pip install

ag_news_csv，AG is a collection of more than 1 million news articles.training samples is 120,000 and testing 7,600.Each class contains 30,000 training samples and 1,900 testing samples.

消息 新闻发布系统（jsp + servlet + mysql） 项目结构： 登录页： 后台管理页： 新闻前端页面：

496,835 条来自 AG 新闻语料库 4 大类别超过 2000 个新闻源的新闻文章，数据集仅仅援用了标题和描述字段。每个类别分别拥有 30,000 个训练样本及 1900 个测试样本。 README： AG's News Topic Classification Dataset Version 3, Updated 09/09/2015 ORIGIN AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html . The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600. The file classes.txt contains a list of classes corresponding to each label. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

官方离线安装包，测试可用。请使用rpm -ivh [rpm完整包名] 进行安装

官方离线安装包，测试可用。请使用rpm -ivh [rpm完整包名] 进行安装

官方离线安装包，测试可用。请使用rpm -ivh [rpm完整包名] 进行安装

官方离线安装包，测试可用。请使用rpm -ivh [rpm完整包名] 进行安装