Name
Abstract | ||
|---|---|---|
In this paper, we derive a new one-class Support Vector Machine (SVM) based on hidden information. Taking into account the fact that in some applications, the training instances are rather limited, we attempt to utilize the additional information hidden in the training data. We demonstrate the performance of the new one-class SVM on several publicly available data sets from UCI machine learning repository and also present the comparison with the standard one-class SVM. The experimental results indicate the validity and advantage of the new one-class SVM. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.knosys.2014.01.002 | Knowl.-Based Syst. |
Keywords | Field | DocType |
training data,new one-class support,new one-class svm,training instance,additional information,available data set,standard one-class svm,uci machine,vector machine,hidden information,svm,support vector machine | Structured support vector machine,Training set,Data mining,Data set,Ranking SVM,Computer science,Support vector machine,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
60, | 0950-7051 | 17 |
PageRank | References | Authors |
0.60 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenxin Zhu | 1 | 17 | 0.94 |
| Ping Zhong | 2 | 40 | 11.34 |