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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | I-Hsuan Peng | en |
| dc.contributor.author | 彭毅軒 | zh_TW |
| dc.date.accessioned | 2021-06-17T07:21:39Z | - |
| dc.date.available | 2024-07-19 | |
| dc.date.copyright | 2019-07-19 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-04 | |
| dc.identifier.citation | [1] Abadi M. et al, TensorFlow: Large-scale Machine Learning on Heterogeneous Systems (https://www.tensorflow.org/), 2015.
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A., Bontempi, G., Calibrating Probability with Undersampling for Unbalanced Classification, in Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015. [12] Dal Pozzolo, A., Adaptive Machine Learning for Credit Card Fraud Detection, Université Libre de Bruxelles Machine Learning Group, PhD thesis (supervised by G. Bontempi), 2015. [13] Dorogush, A. V., Ershov, V., Gulin, A., CatBoost: Gradient Boosting with Categorical Features Support, Proceedings of Neural Information Processing Systems 2018 (NIPS 2018), 2018. [14] Dua, D., Graff, C., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2019. [15] Friedman J. H., Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, 2001, pp. 1189-1232 [16] Ke G. et al, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Proceedings of Neural Information Processing Systems 2017(NIPS 2017), 2017. 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Society for Industrial and Applied Mathematics, 1990. [31] Williams, C. K. I., Barber, D., Bayesian Classification with Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1998, pp. 1342-1351. [32] Yeh, I. C., Lien, C. H., The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients. Expert Systems with Applications, 36(2), 2009, pp. 2473-2480. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73190 | - |
| dc.description.abstract | 高斯過程(Gaussian process)主要被用在解決迴歸(regression)與分類(classification)問題上，在眾多機器學習方法之中預測表現良好，且是非常彈性的非參數(nonparametric)模型。但其擁有一些明顯的缺點，如時間複雜度高，導致不容易應用在資料量較多的問題上。為了解決這個缺點，本次研究將結合高斯過程(Gaussian process)與梯度提升方法(gradient boosting)應用在分類問題上。根據實驗結果顯示，與原本的高斯過程相比，此結合後產生的演算法可大幅增加模型訓練速度，使模型可以在訓練中使用更多的資料，且可在某些情況下增加模型的表現，此外也會探討此方法與其他分類器在不同資料集與不同訓練資料量上之差別。 | zh_TW |
| dc.description.abstract | Gaussian process (GP) is mainly used to solve regression and classification problems in machine learning. GP is a nonparametric model with good prediction performance and wide applications. However, GP has an obvious drawback: high time complexity. This drawback makes it inappropriate for large data. In this work, we combine Gaussian process and gradient boosting to form Gradient Boosting Gaussian Process Classifier (GBGPC), then apply it to classification problems. The experiment results show that the proposed algorithm can largely improve training efficiency and performance in some situation compare to the standard Gaussian process. We also discuss the difference of performance between GBGPC and other well-known classifiers among different datasets and training data sizes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T07:21:39Z (GMT). No. of bitstreams: 1 ntu-108-R06725038-1.pdf: 1234228 bytes, checksum: 61e9662205398d6c8e64a30a6bd9f1a3 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 Chapter 2 Literature Review 3 2.1 Gaussian Process 3 2.1.1 Gaussian Process Regression 4 2.1.2 Gaussian Process Classification 6 2.1.3 Latent Variable Inferences 8 2.1.4 Automatic Relevance Determination 15 2.1.5 Approximation Methods for Gaussian Process 16 2.2 Gradient Boosting Machine 18 2.2.1 Boosting 19 2.2.2 Gradient Boosting 19 2.2.3 Applications and Extensions of Gradient Boosting 21 Chapter 3 Design of Gradient Boosting Gaussian Process Classifier 24 3.1 Loss Function 24 3.2 Algorithm of Gradient Boosting Gaussian Process Classifier 27 3.2.1 Training and Test Algorithm of GBGPC 27 3.2.2 Base Learner Gaussian Process Regressor 30 3.2.3 Batch size 31 3.2.4 Early Stopping 32 Chapter 4 Experiment and Discussion 34 4.1 Datasets 34 4.2 Experiment Design and Baseline Models 35 4.3 Experiment Results 37 4.3.1 Performance Comparison 37 4.3.2 Early Stopping and Training time 48 4.3.3 Pseudo-Response Comparison 51 Chapter 5 Conclusion and Future Work 55 REFERENCE 57 | |
| dc.language.iso | en | |
| dc.subject | 梯度提升 | zh_TW |
| dc.subject | 高斯過程 | zh_TW |
| dc.subject | 分類 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | gradient boosting | en |
| dc.subject | machine learning | en |
| dc.subject | classification | en |
| dc.subject | Gaussian process | en |
| dc.title | 基於高斯過程的梯度提升分類器 | zh_TW |
| dc.title | Gradient Boosting Classifier based on Gaussian Process | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡宇泰(Yu-Tai Chien),陳以錚(Yi-Cheng Chen) | |
| dc.subject.keyword | 高斯過程,分類,機器學習,梯度提升, | zh_TW |
| dc.subject.keyword | Gaussian process,classification,machine learning,gradient boosting, | en |
| dc.relation.page | 61 | |
| dc.identifier.doi | 10.6342/NTU201901204 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-07-04 | |
| 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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