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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31903
Title: | 進階參數混成模型於多標籤文件分類之應用 Advanced Parametric Mixture Model for Multi-Label Text Categorization |
Authors: | Tzu-Hsiang Kao 高子翔 |
Advisor: | 林智仁(Chih-Jen Lin) |
Keyword: | 參數混成模型,多標籤分類,文件分類,機器學習,最大概似機率, parametric mixture model,multi-label classification,text categorization,machine learning,maximum likelihood, |
Publication Year : | 2005 |
Degree: | 碩士 |
Abstract: | This thesis studies Parametric Mixture Models (PMMs). They are efficient statistical models to solve multi-label text categorization problem. Conventional machine learning models usually training binary classifiers for predicting multi-label problem. In contrast, PMMs use a single statistical model to handle multi-label text. We propose an Advanced Parametric Mixture Model (APMM) based on PMMs. Its maximum likelihood is a concave programming problem. We design update rules so that iterations converge to a global maximum. The experiments use the real-world yahoo.com datasets under three common multi-label classification measurements. The results show that APMM is competitive. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31903 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 工業工程學研究所 |
Files in This Item:
File | Size | Format | |
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ntu-94-1.pdf Restricted Access | 264.63 kB | Adobe PDF |
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