請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2427
標題: | 隨機森林與梯度提升決策樹在大數據下之探討 A Study of Random Forests and Gradient Boosting Decision Trees for Large-Scale Data |
作者: | Sheng-Wei Chen 陳聖惟 |
指導教授: | 林智仁(Chih-Jen Lin) |
關鍵字: | 梯度提升決策樹,隨機森林,支持向量機,分類與回歸樹, gradient boosting decision trees,random forests,support vector machine,classification and regression tree, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | In the past, we know that the tree-based methods may not handle the large-scale data sets. Therefore, the solver of the gradient boosting decision trees performs excellent in the large-scale data competitions. To know the details, we analyze the models of these tree-based methods. Furthermore, we compare their test accuracy and training time, we also consider the linear model and kernel method. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2427 |
DOI: | 10.6342/NTU201702065 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 工業工程學研究所 |
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ntu-106-1.pdf | 661.39 kB | Adobe PDF | 檢視/開啟 |
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