A contention-sensitive data structure is a concurrent data structure in which the overhead introduced by locking is eliminated in the common cases, when there is no contention, or ...
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...
Classification is one of the most fundamental problems in machine learning, which aims to separate the data from different classes as far away as possible. A common way to get a g...
Bin Zhang, Fei Wang, Ta-Hsin Li, Wen Jun Yin, Jin ...
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach...
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure maycontain high-dimensional clusters that are related in co...