文件分類技術可以自動化的從已經分類好的訓練範例中學習出分類模式，並藉由此模式，將未分類的文件歸類到正確的類別之中。傳統二分類情況下的文件分類技術，所需要的訓練範例必須包含正範例與負範例，然而，在很多現實情況下，取得負範例需要很昂貴的成本，相較之下，正範例與未分類範例的取得就容易許多。因此，本研究針對現有只以正範例與未分類範例作為學習範例的演算法的限制，利用Ensemble的概念提出了一個混合式的研究架構，並以垃圾郵件過濾器作為我們評估的例子，實證評估結果顯示，本研究確實比PNB與PEBL此兩種演算法能達到更穩定且可靠的分類結果。

Text categorization is the process of assigning new documents to predefined document categories on the basis of a classification model(s) induced from a set of pre-categorized training documents. In a typical dichotomous classification scenario, the set of training documents includes both positive and negative examples; that is, each of the two categories is associated with training documents. However, in many real-world text categorization applications, positive and unlabeled documents are readily available, whereas the acquisition of samples of negative documents is extremely expensive or even impossible. In this study, we propose and develop an ensemble approach, referred to as E2, to address the limitations of existing algorithms for learning from positive and unlabeled training documents. Using the spam email filtering as the evaluation application, our empirical evaluation results suggest that the proposed E2 technique exhibits more stable and reliable performance than PNB and PEBL.

TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 RESEARCH MOTIVATION AND OBJECTIVES 2
1.3 ORGANIZATION OF THE THESIS 4

CHAPTER 2 LITERATURE REVIEW 5
2.1 TEXT CATEGORIZATION 5
2.2 LEARNING FROM POSITIVE AND UNLABELED EXAMPLES 6
2.2.1 Positive Na

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