Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21312Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正(Yen-Jeng Oyang) | |
| dc.contributor.author | Chun-Chieh Yang | en |
| dc.contributor.author | 楊竣傑 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:30:51Z | - |
| dc.date.available | 2030-01-01 | - |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-13 | |
| dc.identifier.citation | [1] A. J. Izenman, 'Recent Developments in Nonparametric Density Estimation,' Journal of the American Statistical Association, vol. 86, no. 413, pp. 205-224, 1991.
[2] R.V. Hogg, J. McKean, A.T. Craig, Introduction to mathematical statistics, 7/e ed., Pearson College Div, 2014. [3] S. Kullback, R.A. Leibler, 'On Information and Sufficiency,' Annals of Mathematical Statistics, vol. 22, p. 79–86, 1951. [4] H. Akaike, 'A new look at the statistical model identification,' in IEEE Transactions on Automatic Control, 1974. [5] Y. Tamura, T. Sato, M. Ooe and M. Ishiguro, 'A procedure for tidal analysis with a Bayesian information criterion,' Geophys. J. Int., vol. 104, pp. 507-516. [6] A. Justel, D. Peña and R. Zamar, 'A multivariate Kolmogorov-Smirnov test of goodness of fit,' Statistics & Probability Letters, vol. 35, no. 3, pp. 251-259, 1997. [7] K. HIROSE, 'Mean integrated squared error and deficiency of nonparamatric recursive kernel estimators of smooth distribution functions,' Nihonkai Mathematical Journal, vol. 8, pp. 195-207, 1997. [8] D. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization, NewYork: Wiley, 1992. [9] D. SCOTT, 'On optimal and data-based histograms,' Biometrika, vol. 66, no. 3, p. 605–610, 1979. [10] E. Wegman, 'Nonparametric probability density estimation,' Journal of Statistical Computation and Simulation, vol. 1, pp. 225-245, 1972. [11] T. Cover and P. Hart, 'Nearest neighbor pattern classification,' in IEEE transaction on Information Theory, 1967. [12] E. Parzen, 'On estimation of a probability density function and mode,' The annals of mathematical statistics, vol. 33, pp. 1065-1076, 1962. [13]G. H. John, P. Langley, 'Estimating continuous distributions in Bayesian classifiers,' in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995. [14] A. Elgammal, R. Duraiswami, D. Harwood and L. Davis, 'Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,' Proceedings of the IEEE, vol. 90, no. 7, pp. 1151-1163, July 2002. [15] B. Silverman, Density estimation for statistics and data analysis, Chapman and Hall/CRC, 1986. [16] G. R. Terrell, D. W. Scott, 'Variable Kernel Density Estimation,' The Annals of Statistics, vol. 20, pp. 1236-1265, 1992. [17] I. Abramson, 'On Bandwidth Variation in Kernel Estimates-A Square Root Law,' The Annals of Statistics, vol. 10, no. 4, pp. 1217-1223, 1982. [18]Y.J. Oyang, Y.Y. Ou, S.C. Hwang, C.Y. Chenl and D. T.H. Chang, 'Data Classification with a Relaxed Model of Variable Kernel Density Estimation,' in n Proc. IEEE Int. Joint Conf. Neural Netw., 2005. [19]G.E.P. Box, G.C. Tiao, Bayesian inference in statistical analysis, Wiley- Interscience, 1992. [20] G.R. Terrel and D.W. Scott, 'Variable Kernel Density Estimation,' The Annals of Statistics, vol. 20, no. 3, pp. 1236-1265, 1992. [21] C. Lih, 'Density Estimation in High Dimensions Using Distance to K Nearest Neighbors,' 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21312 | - |
| dc.description.abstract | 本論文比較幾種核心密度估計方法(Kernel Density Estimation)並提出可變帶寬核密 度估計(Adaptive Density Estimation)的挑選模式。核密度估計是一種無母數統計方 法，相對有母數統計較不受特定框架影響，有較高的彈性和配適性，而可變帶寬 核密度估計較固定帶寬核密度估計有更佳的配適性。論文中討論此兩種核密度估 計方法，並提出 RVKDE 的帶寬優化演算法 Elevated RVKDE，以減少需調整的參 數，在多種人工合成資料集上實驗，結果顯示此方法在大部分情況下表現優於其 他方法;最後文中介紹如何應用密度估計於分類器的機率估計，及應用於實際登 革熱資料集，並和其他分類演算法比較分類能力。 | zh_TW |
| dc.description.abstract | This study compares kernel density estimation (KDE) algorithms which is a branch of nonparametric statistics and propose a method to optimize bandwidth selection in Relaxed Variable Kernel Density Estimation (RVKDE) called Elevated RVKDE. KDE methods have fixed KDE and adaptive KDE. Nonparametric method is flexible and adaptive KDEs have even better goodness of fit than fixed KDEs. However, RVKDE is an adaptive method that have to tune a smoothing parameter to reach the ideal condition. In this study, we propose the method that there’s no need to tune this smoothing parameter anymore and outperforms other KDE methods mostly on synthesis dataset. This study also introduce how to implement density estimation to a probability estimated classifier and compare the performance of it with several machine learning algorithms on the dengue dataset. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:30:51Z (GMT). No. of bitstreams: 1 ntu-108-R06h41012-1.pdf: 1632299 bytes, checksum: fffc1286bbd1e30a32f83b0c149369ef (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | ABSTRACT I
中文摘要 II TABLE OF CONTENTS III LIST OF FIGURES V LIST OF TABLES VII CHAPTER 1 INTRODUCTION 1 1.1 DENSITY ESTIMA TION 1 1.2 EVALUATION CRITERIA 2 CHAPTER 2 LITERATURE REVIEW 4 2.1 HISTOGRAM & NAIVE ESTIMATOR 4 2.2 K NEAREST NEIGHBOR ESTIMATOR 5 2.3 KERNEL DENSITY ESTIMATION 7 2.3.1 Silverman’s rule of thumb 8 2.3.2 Abramson 9 2.3.3RVKDE 9 2.4 PROBABILITY ESTIMATION 10 CHAPTER 3 METHOD 12 3.1 BANDWIDTH SELECTION OF KDE 12 3.2 ELEVATED RVKDE 14 CHAPTER 4 EXPERIMENT & APPLICATION 18 4.1 EXPERIMENTS 18 4.2 APPLICATION TO CLASSIFICATION 20 4.3 DATASET 21 4.4 RESULT 22 CHAPTER 5 CONCLUSION 24 5.1 DISCUSSION AND CONCLUSION 24 5.2 FUTURE WORK 25 REFERENCE 26 APPENDIX I 29 APPENDIX II 32 | |
| dc.language.iso | en | |
| dc.title | 以核密度為基礎推估分類器之預測機 | zh_TW |
| dc.title | Kernel Density Based Probability Estimation for Data Classifiers | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王榮德(Jung-Der Wang),金傳春(Chwan-Chuen King),韓謝忱 | |
| dc.subject.keyword | 無母數統計,核密度估計,分類器,機率估計, | zh_TW |
| dc.subject.keyword | Nonparametric,Kernel Density Estimation,Classifier,Probability Estimation, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU201903248 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-08-14 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| dc.date.embargo-terms | 2030-01-01 | |
| Appears in Collections: | 統計碩士學位學程 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-108-1.pdf Restricted Access | 1.59 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.