Description: 这是一本关于文本挖掘的书籍，包括聚类 分类 信息提取的内容-This is a book on text mining, including clustering classification of the content of information extraction Platform: |
Size: 4111360 |
Author:youyou |
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Description: 首先，对CATEGORY中的txt文件分类；
其次，对多个txt文件中的英文文本进行分词；
最后，通过贝叶斯公式进行分类；-First, in the txt file CATEGORY classification Secondly, multiple txt files in English text word Finally, by Bayes formula to be classified Platform: |
Size: 411648 |
Author:guangyu |
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Description: 6.压缩文件有密码
7.源码重复或已经存在
请不要上传有版权争议的内容和木马病毒代码和访问国外网站的代理软件
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-We study the automatic classification of Web documents into pre-specified categories, with the objective of increasing the precision of Web search. We describe experiments in which we classify
documents into high-level categories of the Yahoo! taxonomy, and a simple search architecture and implementation using this classification. The validation of our classification experiments offers interesting insights into the power of such automatic classification, as well as into the nature
of Web content. Our research indicates that Web classification and search tools must compensate for artifices such as Web spamming that have resulted from the very existence of such tools.
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Platform: |
Size: 653312 |
Author:黄国彬 |
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Description: 用于文本分析和分类的pLSA(Probability Latent Semantic Analysis)的Matlab算法，含有测试数据及算法原理介绍。也可用于图像分析。相比原来的版本，增加了示例程序demo.m，以实现可视化效果，用户更容易使用和理解pLSA算法。-For text analysis and classification of pLSA (Probability Latent Semantic Analysis) of the Matlab algorithm with test data and algorithm theory description. Can also be used for image analysis. Compared to the original version, an increase of sample programs demo.m, to achieve the visual effect, the user can more easily use and understand the algorithm. Platform: |
Size: 302080 |
Author:Tang Ketan |
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Description: 数字图像处理及实例应用，包括图像的边缘检测，阈值分割算法，文字与非文字区域的划分-And examples of digital image processing applications, including edge detection, threshold segmentation algorithm, text classification with non-text area Platform: |
Size: 89088 |
Author:陆颖 |
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Search - text classification with matlab