Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
Active learning strategies can be useful when manual labeling
effort is scarce, as they select the most informative
examples to be annotated first. However, for visual category
...
Sudheendra Vijayanarasimhan (University of Texas a...
It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the ...
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabe...