請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74629
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 童涵浦 | |
dc.contributor.author | Tian-Chung Lin | en |
dc.contributor.author | 林天中 | zh_TW |
dc.date.accessioned | 2021-06-17T08:46:40Z | - |
dc.date.available | 2020-08-12 | |
dc.date.copyright | 2019-08-12 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-06 | |
dc.identifier.citation | Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin, 2012, Learning from Data: a Short Course. AMLbook.com.
Acemoglu, Daron. , Suresh Naidu, Pascual Restrepo, and James A. Robinson. 2019. “Democracy Does Cause Growth.” Journal of Political Economy. 127(1): 47-100. Anckar, Carsten, Cecilia Fredriksson. 2019. “Classifying political regimes 1800-2016: a typology and a new dataset.” European Political Science. 18(1): 84-96. Boix, Carles, Michael Miller, Sebastian Rosato. 2013. “A Complete Data Set of Political Regimes, 1800-2007.” Comparative Political Studies. 46(12):1523-1554. Brock, Guy, Vasyl Pihur, Susmita Datta, Somnath Datta. 2008. “clValid: An R Package for Cluster Validation.” Journal of Statistical Software. 25(4):1-22. Charrad, Malika, Nadia Ghazzali, Veronique Boiteau, Azam Niknafs. 2014. “NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set.” Journal of Statistical Software. 61(6):1-36. Cheibub, José Antonio, Jennifer Gandhi and James Raymond Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice. 143(1-2): 67-101. Coppedge, Michael, John Gerring, David Altman, Michael Bernhard, Steven Fish, Allen Hicken, Matthew Kroenig, Staffan I. Lindberg, Kelly McMann, Pamela Paxton, Holli A. Semetko, Svend-Erik Skaaning, Jeffery Staton, Jan Teorell. 2011. “Conceptualizing and Measuring Democracy: A New Approach.” Perspective on Politics. 9(2): 247-267. Freedom House. 2019. Freedom in the World 2018. The Annual Survey of Political Rights & Civil Liberties. Washington D.C.(US). Freedom House. 2019. Freedom in the World 2019. Washington D.C.(US). Geddes, Barbara, Joseph Wright, Erica Frantz. 2014. “Autocratic Breakdown and Regime Transitions: A New Data Set.” Perspectives on Politics. 12(1): 313-331. Gerring, John, Michael Coppedge, Et Al., Jan Teorell, and Staffan I. Lindberg. Coppedge, Michael, John Gerring, Carl Henrik, Staffan I. Lindberg, Jan Teorell, Kyle L. Marquardt, Juraj Medzihorsky, Daniel Pemstein, Josefine Pernes, Johannes von Romer, Natalia Stepanova, Eitan Tzelgov, Yi-ting Wang, Steven Wilson. 2019. “V-Dem Methodology v9.” Varieties of Democracy (V-Dem) Project. Gründler, Klaus & Tommy Krieger. 2016. “Democracy and Growth: Evidence from a Machine Learning Indicator.” European Journal of Political Economy. 45:85-107. Gründler, Klaus & Tommy Krieger. 2018. “Machine Learning Indices, Political Institutions, and Economic Development.” CESifo Working Paper No. 6930. James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani. 2013. An Intorduction to Statistical Learning with Applications in R. Springer. Marshall, Monty G., Ted Robert Gurr, Keith Jaggers. 2015. “Polity IV Project: Political Regime Characteristics and Transitions, 1800-2015. Dataset Users' Manual.” Center for Systemic Peace. Marshall, Monty G., Gabrielle C. Elzinga-Marshall, 2017, Global Report 2017: Conflict, Governance, and State Fragility. Center for Systemic Peace. Munck, Gerardo L. & Jay Verkuilen. 2002. “Conceptualizing and Measuring Democracy: Evaluating Alternative Indices.” Comparative Political Studies. 35(1): 5-34. Pemstein, Daniel, Meserve, S. A., and Melton, J. 2010. Democratic compromise: A latent variable analysis of ten measures of regime type. Political Analysis.18(4):426-449. Pemstein, Daniel, Marquardt, Kyle L., Tzelgov, Eitan, Wang, Yi-ting, Krusell, Joshua, Miri, Farhad. 2018. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data.” V-Dem Working Paper. Prezeworski, A. 2000. Democracy and Development: Political Institutions and Well-being in the World, 1950-1990. Cambridge University Press. Teorell, Jan, Michael Coppedge, Svend-Erik Skaaning, and Staffan I. Lindberg. 2016. Measuring electoral democracy with v-dem data: Introducing a new polyarchy index. V-Dem Working Paper. Treier, Shawn, Simon Jackman. 2008. “Democracy as a Latent Variable.” American Journal of Political Science. 52(1): 201-217. Vanhanen, Tatu. 2014. “Measures of Democracy 1810-2012.” Finnish Social Science Data Archive. http://www.fsd.uta.fi. Vapnik, Vladimir N. 1995. The Nature of Statistical Learning Theory, Springer-Verlag. Vapnik, Vladimir N. 1998. Statistical Learning Theory, volume1. Wiley New York. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74629 | - |
dc.description.abstract | 民主指標為所有量化民主研究之基礎,如何建立一個客觀反映民主相關資料結構的民主指標為本文探討之問題。本文根據Gründler與Krieger提出之觀點,將資料透過機器學習的演算法導出民主指標,可以避免民主指標在加總計算時,由研究者主觀判斷各要素指標之間的相對重要性,因而使民主指標無法客觀反映民主相關資料。然而,該文透過其他廣泛使用之民主指標中極端民主與極端非民主之國家年代作為預視資料,使預視資料仍隱含其他指標之主觀判斷之結果。故本文目的為透過非監督式學習的叢集分析建立預視資料,使預視資料更能反映資料本身的分布特性,找出民主相關資料中分布最極端之數據,再透過監督式學習的支援向量機和支援向量迴歸建立二分民主指標模型與連續民主指標模型。 | zh_TW |
dc.description.abstract | Democracy index is the foundation of quantitative democracy research. How to build a democracy index which can objectively reflect the structure and feature of democracy data is the main question of this research. This paper will apply the concept of building democracy index by machine learning algorithm which is introduced by Gründler and Krieger. In their point of view, machine learning can avoid the problem of deriving an aggregation method subjectively. However, they do not apply the machine learning algorithm to the methodology of selecting priming data. Instead, they select extreme democracy and non-democracy country-year in other notable democracy index as their priming data, which may still cause the objective problem which they want to solve. In this paper, I will apply unsupervised learning algorithm to construct priming data, and apply the data into supervised learning algorithm to build a new democracy index which can subjectively summarize the democracy data. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:46:40Z (GMT). No. of bitstreams: 1 ntu-108-R03322008-1.pdf: 1826071 bytes, checksum: a2ffa140c4460cd8cc4b5886fe69da5c (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 目錄 1
圖表目錄 3 第一章 緒論 5 第一節 研究動機與問題 5 第二節 章節安排 7 第二章 文獻探討 9 第一節 民主指標測量問題 9 第二節 支援向量機指標 12 第三章 研究方法與資料 15 第一節 研究方法 15 第二節 資料選取 17 第四章 民主指標衡量 25 第一節 遺漏值處理 25 第二節 叢集分析結果 27 第三節 民主指標模型 28 第五章 結論 33 第一節 SVM-DDI與SVM-CDI 33 第二節 指標改善方向 33 參考文獻 37 附錄一 政治實體年份列表 41 附錄二 資料欄位摘要表 45 附錄三 叢集分析各民主要素指標摘要與叢集排序 47 附錄四 遺漏值摘要 51 附錄五 2018 SVR-CDI 53 附錄六 台灣SVM-CDI & SVM-DDI 56 | |
dc.language.iso | zh-TW | |
dc.title | 非監督與監督式學習法訓練民主指標 | zh_TW |
dc.title | Combination of Supervised and Unsupervised Learning for Training the Democracy Index | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 馮勃翰 | |
dc.contributor.oralexamcommittee | 吳文欽,王奕婷 | |
dc.subject.keyword | 民主指標,非監督式學習,監督式學習,支援向量機,叢集分析, | zh_TW |
dc.subject.keyword | democracy index,unsupervised learning,supervised learning,support vector machine,cluster analysis, | en |
dc.relation.page | 57 | |
dc.identifier.doi | 10.6342/NTU201902146 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-06 | |
dc.contributor.author-college | 社會科學院 | zh_TW |
dc.contributor.author-dept | 政治學研究所 | zh_TW |
顯示於系所單位: | 政治學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 1.78 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。