Paper Info
Title
A Novel Evaluation Method Basing on Support Vector Machines
Abstract
Recently support vector machine (SVM) has become a more and more popular classification tool. We presented our two-phase, efficient, and fair evaluation method for DRMs (digital right management system) basing on SVM. Influence of three difference methods and test set number on evaluation result is discussed. After analysized by binary logistic regression, odds ratio comparison of SVM with multi-phase fuzzy synthesized evaluation and FCM illustrates that SVM is the most excellent in these three approaches. Through detailed experimental evaluations under various data set of samples and approaches, our evaluation method of SVM is illustrated to be scalable and accurate.
Year
DOI
Venue
2008
10.1109/MUE.2008.111
MUE
Keywords
Field
DocType
support vector machines,multi-phase fuzzy synthesized evaluation,popular classification tool,evaluation method,novel evaluation method basing,evaluation result,fair evaluation method,digital right management system,binary logistic regression,detailed experimental evaluation,odds ratio comparison,difference method,pervasive computing,fuzzy set theory,machine learning,regression analysis,web services,testing,logistics,odd ratio,support vector machine,graphics,clustering algorithms,svm
Data mining,Ranking SVM,Regression analysis,Computer science,Support vector machine,Fuzzy logic,Fuzzy set,Artificial intelligence,Logistic regression,Machine learning,Test set,Scalability
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
2
Name
Order
Citations
PageRank
Guang-ming Xian1514.58
Bi-qing Zeng2184.83