DocumentCode
464191
Title
Effectiveness of Multi-Perspective Representation Scheme on Support Vector Machines
Author
Zeng, Jia ; Alhajj, Reda
Author_Institution
Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB
Volume
1
fYear
2007
fDate
21-23 May 2007
Firstpage
335
Lastpage
340
Abstract
In this paper, we present a multi-perspective representation (MPR) method, which takes advantage of the synergy of multiple representations of an information object. We have provided a detailed description of how to integrate the MPR scheme with support vector machines (MPR-SVM). The results of the experiments conducted on two benchmark data sets have shown the applicability and effectiveness of using the MPR-SVM scheme for classification purposes.
Keywords
classification; support vector machines; benchmark data sets; classification; information object; multiperspective representation; support vector machines; Artificial neural networks; Biometrics; Computer science; Data mining; Decision trees; Machine learning; Machine learning algorithms; Supervised learning; Support vector machine classification; Support vector machines; classification; multi-perspective representation method.; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on
Conference_Location
Niagara Falls, Ont.
Print_ISBN
978-0-7695-2847-2
Type
conf
DOI
10.1109/AINAW.2007.163
Filename
4221082
Link To Document