Advanced Parametric Mixture Model for Multi-Label Text Categorization
Tzu-Hsiang Kao · 2006
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. ii