Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking f...
— Log-linear models are widely used for labeling feature vectors and graphical models, typically to estimate robust conditional distributions in presence of a large number of pot...
When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural languag...
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
In this paper, we introduce a new instance-based approach to the label ranking problem. This approach is based on a probability model on rankings which is known as the Mallows mode...