We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an...
A complete diagnostic Bayesian network model cannot be achieved and the result of the constructed model cannot be guaranteed unless correct and reliable data are provided to the m...
We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-c...
This paper addresses the question of allocating computational resources among a set of algorithms in order to achieve the best performance on a scheduling problem instance. Our pr...
We consider example-critiquing systems that help people search for their most preferred item in a large catalog. We first analyze how such systems can help users in the framework ...