Abstract—China universities are building informational applications nowadays. Much data are generated anytime and the amount of the data is approaching big data level.
Universities also build public data platform/data center to integrate and synchronize data from many application systems for data outlining and big data analysis.
However, there are many unstructured raw data that cannot be synchronize with public data platform, as amount of them are huge. Also, all the data, never the less synchronized or unsynchronized, can only shared within single university.
To share data between universities, a rapid way is to classify the data of universities from their similar application systems via Internet. So, it is necessary to extract data through internet and classify these data automatically.
Since the data of Educational Resources has their own particularities that must be taken into consideration in classification, this paper put forward a Support Vector Machine (SVM) based education resources automatic classifier that address this issue.
Index Terms—Support vector machine, SVM, education, classification, natural language processing.
Tian Xia is with Shanghai Second Polytechnic University, Shanghai, 201209, China (e-mail: xiatian@sspu.edu.cn).
Cite: Tian Xia, "Support Vector Machine Based Educational Resources Classification," International Journal of Information and Education Technology vol. 6, no. 11, pp. 880-883, 2016.