Name
Abstract | ||
|---|---|---|
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. On the basis of K-nearest neighbor (KNN) algorithm for text classification and support vector machines (SVM) algorithm for text classification, we have studied how they affect topic tracking. Then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. Finally, TDT evaluation method proves that optimal topic tracking performance based on SVM increases by 35.134% more than KNN. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/SKG.2010.39 | SKG |
Keywords | Field | DocType |
optimal topic tracking performance,topic representation,vector space model,topic tracking key technology,optimal value,knn,tdt evaluation,pattern classification,k-nearest neighbor algorithm,tdt evaluation method,text classification,support vector machine algorithm,svm,svm increase,effective model,support vector machine,topic tracking,key technology,text analysis,support vector machines,knearest neighbor,k nearest neighbor,classification algorithms,prototypes | Data mining,Text mining,Computer science,Support vector machine,Artificial intelligence,Support vector machine algorithm,Vector space model,Text categorization,Statistical classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-4189-1 | 1 | 0.37 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengdong Li | 1 | 5 | 1.51 |
| Xueqiang Lv | 2 | 45 | 14.97 |
| Hongwei Wang | 3 | 1 | 0.37 |
| Shi Shuicai | 4 | 11 | 4.54 |