文本分类综述
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This document provides an overview of text classification, including its definition and applications, main methods, and evaluation metrics. Text classification involves assigning texts to predefined categories based on their content. Common methods include feature extraction, vector space models, supervised learning algorithms like Naive Bayes, k-NN, decision trees, and support vector machines. Evaluation metrics include precision, recall, accuracy. The document also lists many reference papers and resources on text classification.
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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文本分类综述
- 1. 文本分类综述 王 斌 中国科学院计算技术研究所 2002 年 12 月
- 3. 文本分类的定义和应用
- 6. 文本分类的方法
- 8. 文本分类的过程 文本表示 训练过程 分类过程 训练文本 统计 统计量 特征表示 学习 分类器 新文本 문서 特征表示 类别
- 17. 特征选择方法的性能比较 (1)
- 18. 特征选择方法的性能比较 (2)
- 23. Naïve Bayes 参数计算 Bayes 公式
- 27. The Widrow-Hoff Classifier Online Learning 类 c 向量的第 j 个分量 x i 的第 j 个分量 Learning Rate Target Value ( 0 or 1)
- 28. Neural Network . . . . . c 1 c 2 c n …… Input Layer Hidden Layer Output Layer Backpropagation
- 29. 支持向量机 Support Vector Machine Support Vector Optimal Separating Hyperplane
- 31. 文本分类的评估指标
- 34. 参考文献