Multi-label learning deals with training instances associated with multiple labels. Many common multi-label algorithms are to treat each label in a crisp manner, being either relevant or irrelevant to an instance, and such label can be called logical label. In contrast, we assume that there is a vector of numerical label behind each multi-label instance, and the numerical label can be treated a...

Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e Sch...

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically derive useful constraints from data. In this paper, we experiment with two constraint-based correction approaches as p...

We provide new evidence on consumer demand for ethical products from experiments conducted in a U.S. grocery store chain. We find that sales of the two most popular coffees rose by almost 10% when they carried a Fair Trade label as compared to a generic placebo label. Demand for the higher priced coffee remained steady when its price was raised by 8%, but demand for the lower priced coffee was ...

Background: According to Cobanoglu et al and Murphy, it is now widely

Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than singlelabel setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algori...

Teaching ESL to adults means being awed every day as we witness the tenacity and perseverance of immigrants carving out better lives for themselves and their families. —Spelleri, 2002

In this paper we describe the system submitted to the closed challenge of the CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. The system that we present extracts syntactic and semantic dependencies independently. Syntactic dependencies are processed with the MaltParser 0.4. Semantic dependencies are processed with a combination of memory-based classifiers. We foc...