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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8577
標題: | 資料超過記憶體容量之大規模線性分類 Large Linear Classification When Data Cannot Fit In Memory |
作者: | Hsaing-Fu Yu 余相甫 |
指導教授: | 林智仁(Chih-Jen Lin) |
關鍵字: | 區塊最佳化,大規模學習,支持向量機, Block minimization,large scale learning,support vector machines, |
出版年 : | 2010 |
學位: | 碩士 |
摘要: | Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. As data cannot fit in memory, many design considerations are very different from those for traditional algorithms. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method. Part of this thesis has appears in Yu et al. (2010). |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8577 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 資訊工程學系 |
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