Advanced Parametric Mixture Model for Multi-Label Text Categorization
Date Issued
2005
Date
2005
Author(s)
Kao, Tzu-Hsiang
DOI
en-US
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.
Subjects
參數混成模型
多標籤分類
機器學習
最大概似機率
parametric mixture model
multi-label classification
text categorization
machine learning
maximum likelihood
Type
thesis
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