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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳光禎 | |
| dc.contributor.author | Che-Yu Lin | en |
| dc.contributor.author | 林哲宇 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:39:42Z | - |
| dc.date.available | 2017-08-24 | |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-16 | |
| dc.identifier.citation | [1] D. Donoho, ”50 years of data science,” Turkey Centennial workshop, Sep. 2015.
[2] B. Babadi, N. Kalouptsidis and V. Tarokh, ”SPARLS: The sparse RLS algorithm,” IEEE Trans. Signal Process., vol. 58, no. 8, pp. 4013–4025, Aug. 2010. [3] C. J. Quinn, N. Kiyavash and T. P. Coleman. ”Directed information graphs,” in 2015, [online] Available: https://arxiv.org/abs/1204.2003v2. [4] X. Wang, C. van Eeden and J. V. Zidek. ”Asymptotic properties of maximum weighted likelihood estimators,” Journ. of Statistical Planning and Inference, pp. 37–54, 2004. [5] J. L. Wang et al., ”Online feature selection and its applications,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 698–710, Mar. 2014. [6] A. Ralescu. ”Measuring proximity between heterogeneous data,” Proc. of FUZZ-IEEE, 2004, Jul. 2007. [7] J. Gama et al., ”A survey on concept drift adaption,” ACM Computing Surveys, vol. 46, no. 4, article 44, Mar. 2014. [8] K. C. Chen et al., ”Communication Theoretic Inference on Heterogeneous Data.” IEEE Int. Conf. on Commun., May 2016. [9] J. A. Garcia et al., ”Plant identification via adaptive combination of transversal filters,” Signal Proc., vol. 86, no. 9, pp. 2430–-2438, 2006. [10] S.-L. Huang et al., ”Efficient statistics: extracting information from IID observations,” Allerton Annu. Conf. on Commu., Control and Computing, Oct. 2014. [11] S.-L. Huang and L. Zheng, ”Linear information coupling problems,” Proc. of the IEEE Int. Symp. on Inform. Theory, July 2012. [12] M. Cuturi, ”Fast Global Alignment Kernels,” Proc. of the Int. Conf. on Mach. Learning, 2011. [13] G. Ditzler et al. ”Learning in nonstationary environments: A survey”, Computational Intell. Mag. IEEE, vol. 10, no. 4, pp. 12–25, 2015. [14] S. Haykin, Adaptive Filter Theory, Prentice-Hall, 2002. [15] T. Cover and J. Thomas, Elements of Information Theory, New York: Wiley Interscience, 2006. [16] G. Casella and R. L. Berger, Statistical Inference, 2nd. ed., Thomson Learning, 2002. [17] W. Schindler, K. Lemke and C. Paar, ”A stochastic model for differential side channel cryptanalysis,” In: Rao, J.R., Sunar, B. (eds.): Cryptographic Hardware and Embedded Systems—CHES 2005, 7th Int. Workshop, Edinburgh, UK, Aug. 29 – Sep. 1, 2005, Proc., Lecture Notes in Comput. Sci., vol. 3659, pp. 30–46. Springer, Berlin(2005). [18] P. Kocher, ”Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems,” Advances in Cryptology: Proc. of CRYPTO’96, Springer-Verlag, pp. 104–113, Aug. 1996. [19] D. Brumley and D. Boneh, ”Remote timing attacks are practical,” Comput. Networks: The Int. Journ. of Comput. and Telecommun. Networking, vol. 48, no. 5,p.701–716, Aug. 2005. [20] R. E. Kalman, ”A new approach to linear filtering and prediction problems,” J. Basic Eng., vol. 82, no. 1, pp. 35–45, Mar. 1960. [21] M. S. Arulampalam et al., ”A tutorial on particle filters for online nonlinear / non-Gaussian Bayesian tracking,” IEEE Trans. on Signal Process., vol. 50, no. 2, pp. 174–188, Feb. 2002. [22] S. Leung and C. F. So, ”Gradient-based variable forgetting factor RLS algorithm in time-varying environments,” IEEE Trans. on Signal Process., vol. 53, no. 8, pp. 3141–3150, Aug. 2005. [23] G. M. Kuan, ”Lecture on the Markov switching model,” Working paper, Institute of Economics, Academia Sinica, Taiwan, 2002. [24] R. F. Engle, ”Autoregressive conditional heteroscedasticity with estimates of the variance of inflationary expectations,” Econometrica, vol. 50, no. 4, pp. 987–1007, Jul. 1982. [25] H. Akaike, ”Information theory and an extension of the maximum likelihood principle,” in Petrov, B.N.; Csáki, F., 2nd International Symposium on Information Theory, Tsahkadsor, Armenia, USSR, Sep. 2–8, 1971, Budapest: Akadémiai Kiadó, pp. 267–-281, 1973. [26] K. P. Burnham and D. R. Anderson, ”Multimodel inference – understanding AIC and BIC in model selection,” Sociol. Methods and Research, vol. 33, no. 2, pp. 261–304, Nov. 2004. [27] I. Guyon and A. Elisseeff, ”An introduction to variable and feature selection,” The Journ. of Mach. Learning Research 3, pp. 1157–1182, Jan. 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68873 | - |
| dc.description.abstract | 在本篇論文中，我們研究下列的問題：在非穩定環境下，如何利用其他觀測到的異質變數預測目標時間序列。在資訊爆炸的時代，資料產生機制處於非穩定的狀態在許多情況下皆可遇見，尤其是受人類行為影響的情況下。因此，不斷更新並從資料中學習到最新的觀念(concept) 是不可避免的。基於通訊系統中的信道 (channel)，我們視不同時間序列的資料為不同旁道 (side channel) 產生的輸出，並利用消息理論來萃取其中包含我們所關心的目標的資訊。除了萃取資訊的機制，我們更進一步對其篩選，並將選出確實包含訊息的資訊結合以達到預測的目的。 | zh_TW |
| dc.description.abstract | In this thesis, we research into the problem: how to predict a time series variable with heterogeneous sources under time varying environment. In this information explosive era, the non-stationary issue of the data generating mechanism is ubiquitous, especially data with human activities involved, and making learned concept catch up the change is thus inevitable. Basing on the
concept of communication channel, we view the different data sources as output from different side channels, and applying information theoretic methods to extract information about the target we are interested in from these channels. With extracted information, a wrapper type selecting mechanism for sifting out non-informative ones, followed by an information combining procedure for fusing the information. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:39:42Z (GMT). No. of bitstreams: 1 ntu-106-R04942097-1.pdf: 11966700 bytes, checksum: 3388cce082b8f825a6e53e858dbecf3c (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 iii
誌謝 v Acknowledgements vii 摘要 ix Abstract xi 1 Introduction 1 1.1 Background Introduction 1 1.1.1 Science of Data Analysis 1 1.1.2 Full Scope of Data Science 2 1.2 Types of Problems and Tasks in Data Analytics 3 1.2.1 Supervised Learning 3 1.2.2 Unsupervised Learning 3 1.2.3 Reinforcement Learning 3 1.3 Encountered Difficulties 3 1.3.1 Heterogeneity 4 1.3.2 Non-stationarity 5 1.3.3 Time Series Prediction Under Heterogeneity and Non-stationarity 6 1.4 Literature Survey 6 1.4.1 Learning under Concept Drift – Modelling and Estimation 6 1.4.2 Learning with Heterogeneous Data Sources – Feature Selection 8 1.4.3 Learning under Concept Drift with Heterogeneous Data Sources 9 1.5 Main Contribution 10 2 Preliminary 11 2.1 Estimation 11 2.2 Information Theory 14 3 Methodology 17 3.1 Learning as an Information Processing Procedure 17 3.1.1 Side Channels Inference 19 3.1.2 Issues to Resolve 19 3.2 Information Coupling Filtering 23 3.2.1 Multiple Sources 24 3.3 Generalizing the Model Assumptions of ICF 27 3.3.1 From Additive Noise Channel to More General One 27 3.3.2 From Linear to General Functional 27 3.3.3 From Dim(U) = 1 to Dim(U) > 1 29 3.3.4 Information Extraction with Adopting the Generalization 30 3.4 Generalizing the Information Processing Procedure of ICF 34 3.4.1 Selecting theta_ i that Contains Additional Information 34 3.4.2 Combination of Selected theta_i ’s 36 4 Case Study 39 4.1 Case 1: Predict Air Quality with Geographical Data 39 4.1.1 Model Assumption 39 4.1.2 Results 41 4.1.3 Summary 43 4.2 Case 2: Predict Congestion with Lane Occupancy Data 45 4.2.1 Model Assumption 45 4.2.2 Result 47 4.2.3 Summary 50 4.3 Case 3: Prediction of Connected APs 51 4.3.1 Simulation Setting 52 4.3.2 Model Assumption 55 5 Summary 61 Bibliography 63 | |
| dc.language.iso | en | |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 旁道信息推論 | zh_TW |
| dc.subject | 預測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | time series | en |
| dc.subject | machine learning | en |
| dc.subject | prediction | en |
| dc.subject | Side channels inference | en |
| dc.title | 非穩定環境下之旁道信息推論 | zh_TW |
| dc.title | Side Channels Inference under Non-stationarity | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李志鵬,黃家齊,林嘉慶,張寶基 | |
| dc.subject.keyword | 旁道信息推論,機器學習,時間序列,預測, | zh_TW |
| dc.subject.keyword | Side channels inference,machine learning,time series,prediction, | en |
| dc.relation.page | 65 | |
| dc.identifier.doi | 10.6342/NTU201703386 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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