图 书 馆
利用特征之间的高阶相关性进行文本分类Leveraging Higher Order Dependencies Between Features for Text Classification |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_pottenger_lhodbftc/ |
| 主讲教师: | William M. Pottenger |
| 开课单位: | 新泽西州立大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Traditional machine learning methods only consider relationships between feature values within individual data instances while disregarding the dependencies that link features across instances. In this work, we develop a general approach to supervised learning by leveraging higher-order dependencies between features. We introduce a novel Bayesian framework for classification named Higher Order Naive Bayes (HONB). Unlike approaches that assume data instances are independent, HONB leverages co-occurrence relations between feature values across different instances. Additionally, we generalize our framework by developing a novel data-driven space transformation that allows any classifier operating in vector spaces to take advantage of these higher-order co-occurrence relations. Results obtained on several benchmark text corpora demonstrate that higher-order approaches achieve significant improvements in classification accuracy over the baseline (first-order) methods. |
| 关 键 词: | 机器学习; 了跨实例链接; 高阶朴素贝叶斯 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-27：lxf |
| 阅读次数: | 149 |