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博碩士論文 etd-0911112-150630 詳細資訊
Title page for etd-0911112-150630
論文名稱
Title
臺灣的大學聯考之學系錄取分數預測
he Prediction of the Department Score of the College Entrance Examination in Taiwan
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
46
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2012-09-07
繳交日期
Date of Submission
2012-09-11
關鍵字
Keywords
支持向量機、支持向量回歸機、回歸、大學聯考、預測
SVM, SVR, College Entrance Examination, regression, prediction
統計
Statistics
本論文已被瀏覽 5729 次,被下載 455
The thesis/dissertation has been browsed 5729 times, has been downloaded 455 times.
中文摘要
七月是臺灣畢業季節,每年在這個時候,都會有大量的指定科目考試考生使用各式各樣的落點預測系統。有非常多的網站或者補習班提供收費或者免費的落點預測服務,這些預測系統根據考生輸入的成績,給予考生志願選填的建議;但是大部分的預測系統都並非建立於嚴謹的理論驗證上,為此,我們將在本篇論文提出一理論並對其做進一步的分析與比較。在2005年,任眉眉等學者利用統計的理論建立了一個預測模型。林家立在2008又更進一步的修正並改進她們的預測模型。在本篇論文中,我們將介紹的臺灣的目前大學入學機制,並且說明要如何在這種機制下構建一個預測系統,且說明我們先前用經驗法則所建立的預測方法,以及嘗試用支持向量回歸機改進的方法。最後,我們將會使用一個公平的評估方法去比較與分析我們建立的預測系統與其他系統。在我們的實驗中,我們使用2004至2008年在中大考中心提供的分數與所有資訊,並使用根均方誤差值進行評估。此外,我們證明該方法除了可以進行落點預測外,也可以作為一種學校與系所的評估方法。
Abstract
Prediction systems for College Entrance Examination (CEE) are popular during the graduating season, July every year in Taiwan. These systems give students suggestion according to their examination scores. There are several CEE prediction systems in Taiwan, but most of them are not constructed with rigorous theories. In 2005, Zen et al. constructed a prediction model using statistical method, which was later verified and improved by Lin in 2008. In this thesis, we will introduce the recording mechanism of the College Entrance Examination, and explain how to construct a prediction system under this mechanism. Also, we will compare the previous system with ours. We apply an empirical method and SVR as our first two approaches, and then we propose a new method. In our experiments, we consider the scores published by CEE center from 2004 to 2008. We use the root mean square error (RMSE) value to evaluate the performance of our present method. We also use the value generated by our method to show some information of the schools and the departments.
目次 Table of Contents
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 The Backward Method . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Lin’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Prediction with SVR . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter 3. A Prediction Method for College Entrance Examination . . . . . . . .14
3.1 Predicted Unweighted Score . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 20
Chapter 4. Experimental Result . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 5. Conclusion and Future Work . . . . . . . . . . . . . . . . . 27
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
參考文獻 References
[1] T. W. Anderson, An introduction to multivariate statistical analysis, Vol. 3. Wiley New York, 2003.
[2] D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,” Neural Information Processing–Letters and Reviews, Vol. 11, No. 10, pp. 203–224, 2007.
[3] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[4] N. Cristianini and J. Shawe-Taylor, An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge University Press,2000.
[5] N. R. Draper and H. Smith, “Applied regression analysis,” 1998.
[6] M. Hearst, S. Dumais, E. Osman, J. Platt, and B. Scholkopf, “Support vector machines,” Intelligent Systems and their Applications, IEEE, Vol. 13, No. 4, pp. 18–28, 1998.
[7] T. Joachims, “Text categorization with support vector machines: Learning with many relevant features,” Machine Learning: ECML-98, pp. 137–142, 1998.
[8] Joint Board, College Recruitment Commission, “JBCRC.” http://www.jbcrc.edu.tw/.
[9] C.-L. Lin, “On the study of forecasting indices after the college entrance examination with real data,” Master’s thesis, National Cheng Kung University, Tainan, Taiwan, 2008.
[10] J. Neter, M. Kutner, and W. Wasserman, “Applied linear regression models,” Vol. 3, 1996.
[11] A. J. Smola and B. Schlkopf, Learning with kernels. Citeseer, 1998.
[12] A. J. Smola and B. Schlkopf, “A tutorial on support vector regression,” Statistics and computing, Vol. 14, No. 3, pp. 199–222, 2004. 28
[13] The College Entrance Examination Center, “CEEC.” http://www.ceec.edu.tw/, 1989.
[14] Universities Admissions Center. http://www.uac.edu.tw/.
[15] M.-M. Zen, Z.-S. Chen, and H.-H. Jhan, “Statistical forecasting after the college entrance examination,” Journal of the Chinese Statistical Association, Vol. 46, No. 2, pp. 165–181, 2005.
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