Unsupervised learning algorithms have been derived for several statistical models of English grammar, but their computational complexity makes applying them to large data sets int...
Categorizing web-based videos is an important yet challenging task. The difficulties arise from large data diversity within a category, lack of labeled data, and degradation of vi...
Abstract: Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In mul...
This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-...
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key a...