Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification tech...
Tie-Yan Liu, Yiming Yang, Hao Wan, Qian Zhou, Bin ...
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over a...
Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the major...
Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus ...
This paper reports our knowledge-ignorant machine learning approach to the triage task in TREC2004 genomics track, which is actually a text categorization problem. We applied Supp...