Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document’s rank. Fu...
Jianhan Zhu, Jun Wang, Michael J. Taylor, Ingemar ...
This poster investigates the use of theoretical benchmarks to describe the matching functions of XML retrieval systems and the properties of specificity and exhaustivity in XML r...
We describe a method for volume rendering using a spectral representation of colour instead of the traditional RGB model. It is shown how to use this framework for a novel explora...
In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the ...
We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universa...