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https://ah.lib.nccu.edu.tw/handle/140.119/119155| 題名: | 優化資料清理與機器學習的機制 The refined mechanism for data cleaning and machine learning |
作者: | 余艾玨 Yu, Ai-Chueh |
貢獻者: | 蔡瑞煌 Tsaih, Rua-Huan 余艾玨 Yu, Ai-Chueh |
關鍵詞: | 人工神經網路 正規化 單一隱藏層倒傳遞神經網路 Artificial neural networks Regularization Single-hidden layer feed-forward neural networks Resistant learning with envelope module |
日期: | 2018 | 上傳時間: | 2-Aug-2018 | 摘要: | 近年來人工智慧在機器學習的應用扮演重要的角色，而相較於大數據分析的統計方法，ANN成為最有用方法中的其中一個，為了處理動態環境中的時間序列資料和離群值，Wu (2017)提出一個資料清理和機器學習的機制，實驗結果顯示提出的機制在資料清理和機器學習方面是很有效的，Wu (2017)已經透過單一隱藏層倒傳遞神經網路實作RLEM，這個研究將使用兩個方法優化此機制，一個是在RLEM的損失函數(loss function)加上正規化項來避免過度擬合(overfitting)的問題，另一個是修改RLEM並透過新版的Tensorflow實作來達成目標。 In recent years, artificial intelligence (AI) has become an important part in the application of machine learning, and the artificial neural networks (ANN) serves as one of the most useful methods compared to statistical methods for the purpose of big data analytics. To cope with the time series data that may have concept-drifting phenomenon and outliers, Wu (2017) had derived a mechanism for effective data cleaning and machine learning. The experiment results had shown that the proposed mechanism is promising in effective data cleaning and machine learning. Wu (2017) had implemented the resistant learning with envelope module (RLEM) via the adaptive single-hidden layer feed-forward neural networks (SLFN). This research will add the regularization term to loss function to prevent overfitting and will refine RLEM to improve the accuracy of the predicted return of carry trade. The refined mechanism will be implemented via the updated version of Tensorflow. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0105356017 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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| 601701.pdf | 2.09 MB | Adobe PDF2 | View/Open |
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