— The success of sampling-based motion planners has resulted in a plethora of methods for improving planning components, such as sampling and connection strategies, local planner...
In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can uti...
We study the online clustering problem where data items arrive in an online fashion. The algorithm maintains a clustering of data items into similarity classes. Upon arrival of v, ...
We provide a new simpler approach to the on-line load balancing problem in the case of restricted assignment of temporary weighted tasks. The approach is very general and allows to...
Pierluigi Crescenzi, Giorgio Gambosi, Gaia Nicosia...
We introduce a new dimension to the widely studied on-line approximate string matching problem, by introducing an error threshold parameter so that the algorithm is allowed to mis...