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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | Wei-Chun Liao | en |
| dc.contributor.author | 廖維君 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:34:45Z | - |
| dc.date.available | 2023-08-16 | |
| dc.date.copyright | 2018-08-16 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-09 | |
| dc.identifier.citation | Bernardo, J., & Berger, J. (1998). Regression and classification using Gaussian process priors. Bayesian statistics, 6, 475.
Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B., & Lamere, P. (2011). The Million Song Dataset. Retrieved from: https://labrosa.ee.columbia.edu/millionsong/ Breiman, L. (1997). Arcing the edge. Retrieved from Technical Report 486, Statistics Department, University of California at Berkeley.: Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Chalupka, K., Williams, C. K., & Murray, I. (2013). A framework for evaluating approximation methods for Gaussian process regression. Journal of Machine Learning Research, 14(Feb), 333-350. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (pp. 785-794). ACM. Dheeru, D. K. T., Efi. (2017). UCI Machine Learning Repository. Retrieved from: http://archive.ics.uci.edu/ml Fanaee-T, H., & Gama, J. (2014). Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, 2(2-3), 113-127. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378. Gibbs, M. N., & MacKay, D. J. (2000). Variational Gaussian process classifiers. IEEE Transactions on Neural Networks, 11(6), 1458-1464. Jiang, J., Jiang, J., Cui, B., & Zhang, C. (2017). TencentBoost: A Gradient Boosting Tree System with Parameter Server. 2017 IEEE 33rd International Conference on Data Engineering (ICDE). (pp. 281-284). IEEE. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., . . . Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems. (pp. 3149-3157). Li, P., Wu, Q., & Burges, C. J. (2008). Mcrank: Learning to rank using multiple classification and gradient boosting. Advances in neural information processing systems. (pp. 897-904). Meng, X. (2014). MLlib: Scalable machine learning on Spark. Paper presented at the Spark workshop. Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. Proceedings of the Seventeenth conference on Uncertainty in Artificial Intelligence. (pp. 362-369). Morgan Kaufmann Publishers Inc. Nguyen-Tuong, D., Seeger, M., & Peters, J. (2009). Model learning with local gaussian process regression. Advanced Robotics, 23(15), 2015-2034. Nickisch, H., & Rasmussen, C. E. (2008). Approximations for binary Gaussian process classification. Journal of Machine Learning Research, 9(Oct), 2035-2078. Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78(9), 1481-1497. Quiñonero-Candela, J., & Rasmussen, C. E. (2005). A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6(Dec), 1939-1959. Quinonero-Candela, J., Rasmussen, C. E., & Williams, C. K. (2007). Approximation methods for Gaussian process regression. Large-scale kernel machines, 203-224. Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1): MIT press Cambridge. Ridgeway, G. (2017). Generalized Boosted Models: A guide to the gbm package. Retrieved from https://cran.r-project.org/package=gbm Seeger, M., Williams, C., & Lawrence, N. (2003). Fast forward selection to speed up sparse Gaussian process regression. Artificial Intelligence and Statistics 9(EPFL-CONF-161318). Snelson, E., & Ghahramani, Z. (2006). Sparse Gaussian processes using pseudo-inputs. Advances in neural information processing systems (pp. 1257-1264). Snelson, E., & Ghahramani, Z. (2007). Local and global sparse Gaussian process approximations. Artificial Intelligence and Statistics. (pp. 524-531). Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2010). Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE transactions on Biomedical Engineering, 57(4), 884-893. Wahba, G. (1990). Spline models for observational data (Vol. 59): Philadelphia: Society for Industrial and Applied Mathematics. Williams, C. K., & Barber, D. (1998). Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1342-1351. Yeh, I.-C. (1998). Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research, 28(12), 1797-1808. Yeh, I.-C. (2007). Modeling slump flow of concrete using second-order regressions and artificial neural networks. Cement and Concrete Composites, 29(6), 474-480. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70681 | - |
| dc.description.abstract | 高斯過程迴歸 (Gaussian Process Regression) 是機器學習中的一種方法，此方法具有良好的預測結果、且容易實作，但在運算時時間及空間的複雜度高，使得此方法難以被實際運用在大量資料集上。本研究提供一個基於梯度提升演算法的估計方法，實驗結果顯示此方法能夠在訓練時使用較低的時間及記憶體成本來達到良好的估計效果。 | zh_TW |
| dc.description.abstract | Gaussian process regression (GPR) is an important model in the field of machine learning. GPR model is flexible, robust, and easy to implement. However, it suffers from expensive computational cost: O(n^3) for training time, O(n^2) for training memory and O(n) for testing time, where n is the number of observations in training data. In this work, we develop a fast approximation method to reduce the time and space complexity. The proposed method is related to the design of gradient boosting algorithm. We conduct experiments using real-world dataset and demonstrate that the proposed method can achieve comparable prediction performance compared to the standard GPR model and some state-of-the-art regression methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:34:45Z (GMT). No. of bitstreams: 1 ntu-107-R05725020-1.pdf: 1741983 bytes, checksum: ed03cd5dbaf394042d9510f03d1ef27f (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii ABSTRACT iv Contents v List of Tables vii List of Figures viii Chaper 1. Introduction 1 Chaper 2. Literature Review 3 2.1. Gaussian Processes for Machine Learning 3 2.1.1. Gaussian Process Regression 4 2.1.2. Gaussian Process Classification 5 2.1.3. Approximation Methods 6 2.2. Gradient Boosting Algorithms 8 2.2.1. Boosting 8 2.2.2. Gradient Boosting 8 2.2.3. Applications of Gradient Boosting Machine 10 2.2.4. Gradient Boosting Machine Implementation 11 Chaper 3. Design of Gradient Boosting Gaussian Process Regression 12 3.1. Gradient Boosting Gaussian Process Regression 12 3.2. GBGPR for Time Series Forecasting 16 Chaper 4. Dataset 19 Chaper 5. Experimental Results and Discussion 21 5.1. Regression 21 5.1.1. Experimental Results 21 5.1.2. Comparison with Baseline Algorithms 26 5.1.3. Interpretation of Results 27 5.2. Time Series Forecasting 29 Chaper 6. Conclusion and Future Work 31 Reference 32 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 高斯過程迴歸 | zh_TW |
| dc.subject | 梯度提升 | zh_TW |
| dc.subject | 估計 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Approximations | en |
| dc.subject | Gradient Boosting | en |
| dc.subject | Gaussian Process Regression | en |
| dc.title | 基於高斯過程迴歸的梯度提升演算法 | zh_TW |
| dc.title | A Gradient Boosting Algorithm Based on Gaussian Process Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 余峻瑜(Jiun-Yu Yu),洪為璽(Wei-Hsi Hung) | |
| dc.subject.keyword | 機器學習,高斯過程迴歸,梯度提升,估計, | zh_TW |
| dc.subject.keyword | Machine learning,Gaussian Process Regression,Gradient Boosting,Approximations, | en |
| dc.relation.page | 34 | |
| dc.identifier.doi | 10.6342/NTU201802714 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-10 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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