Tasks of information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be formulated in a con...
Supervised methods for learning an embedding aim to map high-dimensional images to a space in which perceptually similar observations have high measurable similarity. Most approac...
Graham Taylor, Ian Spiro, Rob Fergus, Christoph Br...
This paper proposes a method for automatic maintaining the similarity information for a particular class of Flexible Query Answering Systems (FQAS). The paper describes the three m...
Adapting to rank address the the problem of insufficient domainspecific labeled training data in learning to rank. However, the initial study shows that adaptation is not always...
Keke Chen, Jing Bai, Srihari Reddy, Belle L. Tseng
Similarity metrics that are learned from labeled training
data can be advantageous in terms of performance
and/or efficiency. These learned metrics can then be used
in conjuncti...