We boost the efficiency and robustness of distributionbased matching by random subsampling which results in the minimum number of samples required to achieve a specified probabili...
Space constrained optimization problems arise in a variety of applications, ranging from databases to ubiquitous computing. Typically, these problems involve selecting a set of it...
Themis Palpanas, Nick Koudas, Alberto O. Mendelzon
The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval. Recently, a generalization of thi...
Qingping Tao, Stephen D. Scott, N. V. Vinodchandra...
We address a new learning problem where the goal is to build a predictive model that minimizes prediction time (the time taken to make a prediction) subject to a constraint on mod...
Biswanath Panda, Mirek Riedewald, Johannes Gehrke,...
Abstract Given an edge-weighted transportation network G and a list of transportation requests L, the Stacker Crane Problem is to find a minimum-cost tour for a server along the e...
Amin Coja-Oghlan, Sven Oliver Krumke, Till Nierhof...