隨著電子化文件不斷的增多，如何快速準確處理該類型文件，並應用電子化文件易於傳播運用之特性，提供使用者更友善方便之服務，也成為極為重要之學術議題；但也因為電子化文件的成長迅速的特性，傳統上運用人力進行資訊處理的方式與技巧，早已無法負荷如海潮般不停湧入的資訊，更遑論提供使用者足夠即時性的資訊服務。

在此種環境背景之下，各種運用電腦技術的文件自動化處理的機制自然也就蘊孕而生，從利用全文檢索引擎提供使用者進行資訊查檢到自動標示切截重點的文件摘要技術，無一不是使用者面對浩瀚的知識海洋時的一盞救命明燈，而其中利用演算法學習分類概念並將資訊進行分類的自動分類技術，更是將提供使用者直觀、主動的資訊運用模式。

是故，本論文主題即在於利用本論文所提之文件自我擴展策略，用以提升自動分類系統之成效並降低運用自動分類系統之成本，以期達成令自動分類系統更加普及之目標。

第一章 緒論 4
第一節 研究背景 5
第二節 研究目的 7
第三節 研究問題 8
第四節 詞彙定義 9
一、文件自動分類(Text Automatic Classification) 9
二、KNN演算法(K-Nearest Neighbors) 11
三、SVM演算法(Support Vector Machine) 12
四、文件自我擴展(Document Self-Expansion) 13
第二章 文獻分析 14
第一節 文件自動分類簡介 14
一、分類器 15
二、分類測試集 16
三、分類參數 18
第二節 文件擴展相關研究 21
第三節 本章小結 24
第三章 研究方法與實驗設計 25
第一節 研究方法 25
第二節 實驗設計 26
一、文件擴展模式 27
二、分類器選擇 32
三、測試文件集 34
四、實驗參數設計 38
五、評估標準 40
第四章 實驗結果與分析 42
第一節 文件自我擴展實驗結果 42
第二節 結果分析 45
第五章 結論與建議 48
第一節 結論 48
第二節 建議 50
參考書目 51
附錄一：News資料集文件範例與擴展範例 56
附錄二：WebDes資料集文件範例與擴展範例 58
附錄三：LawData資料集文件範例與擴展範例 59

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Platt, J. “Fast Training of SVMs using Sequential Minimal Optimization,” in B. Scholkopf, C. Burges, and A. Smola (Eds.) Advances in Kernel Methods – Support Vector Learning, MIT Press, 1998.
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