Abstract— We propose a novel hierarchical structured prediction approach for ranking images of faces based on attributes. We view ranking as a bipartite graph matching problem; l...
Subspace learning is very important in today's world of information overload. Distinguishing between categories within a subset of a large data repository such as the web and ...
Nandita Tripathi, Michael P. Oakes, Stefan Wermter
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
—For historical documents, available transcriptions typically are inaccurate when compared with the scanned document images. Not only the position of the words and sentences are ...
As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy [...