Classification of Semi-structured Documents

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Daraei, Bardia | 2010

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 41087 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Movaghar Rahimabadi, Ali
Abstract:
Semi-structured documents are a new type of textual documents which has gained a lot of attention nowadays to itself. A specific document modeling for boosting classifiers is needed more than ever which reflects major document specifications. The main goal of this thesis is presenting new adaptive model based on semi-structured documents features. We also aim to use some problem solving approaches such as Statistical approach, Machine Learning and few Algorithmic solutions. In some cases 10% precision optimization can be seen compare to the best approaches available nowadays
Keywords:
Data Mining ; Classification ; Clustering ; Probability Model ; Machine Learning ; Semi-Structured Documents

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