Name
Abstract | ||
|---|---|---|
Webpage categorization has turned out to be an important topic in recent years. In a webpage, text is usually the main content, so that auto text categorization (ATC) becomes the key technique to such a task. For Chinese text categorization as well as Chinese webpage categorization, one of the basic and urgent problems is the construction of a good benchmark corpus. In this study, a machine learning approach is presented to refine a corpus for Chinese webpage categorization, where the AdaBoost algorithm is adopted to identify outliers in the corpus. The standard k nearest neighbor (kNN) algorithm under a vector space model (VSM) is adopted to construct a webpage categorization system. Simulation results as well as manual investigation of the identified outliers reveal that the presented method works well. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_19 | ICNC (1) |
Keywords | Field | DocType |
webpage categorization system,auto text categorization,chinese webpage categorization,adaboost algorithm,chinese text categorization,good benchmark corpus,key technique,important topic,main content,webpage categorization,k nearest neighbor,vector space model,machine learning | k-nearest neighbors algorithm,Categorization,Adaboost algorithm,Web page,Computer science,Outlier,Auto-text,Artificial intelligence,Vector space model,Text categorization,Machine learning | Conference |
Volume | ISSN | ISBN |
3610 | 0302-9743 | 3-540-28323-4 |
Citations | PageRank | References |
1 | 0.36 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dingsheng Luo | 1 | 46 | 11.61 |
| Xinhao Wang | 2 | 57 | 15.23 |
| Xihong Wu | 3 | 279 | 53.02 |
| Huisheng Chi | 4 | 211 | 22.81 |