An improved Learning Evaluation system based on SVM for E-learning

Author

Yuanhong Wu ; Qifeng Nian ; Shenming Gu

Author_Institution

Sch. of Math., Zhejiang Ocean Univ., Zhoushan, China

fYear

2012

fDate

18-20 Oct. 2012

Firstpage

527

Lastpage

529

Abstract

E-learning Learning Evaluation using Principal Component Analysis (PCA) and support vector machine (SVM) is proposed in this paper. In the first step, PCA is employed for dimension reduction and in the second, SVM is employed for classification purpose, resulting in PCA-SVM hybrid model. Experimental results have verified the effectiveness and rationality of the proposed methods.

Keywords

computer aided instruction; data reduction; pattern classification; principal component analysis; support vector machines; PCA-SVM; classification purpose; dimension reduction; e-learning learning evaluation; principal component analysis; support vector machine; Educational institutions; Electronic learning; Indexes; Kernel; Principal component analysis; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-1743-6

Type

conf

DOI

10.1109/ICACI.2012.6463219

Filename

6463219

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=596637