Communication latencies have been identified as one of the performance limiting factors of message passing applications in clusters of workstations/multiprocessors. On the receiver...
Felix Freitag, Montse Farreras, Toni Cortes, Jes&u...
Abstract. Hybrid recommender systems are capable of providing better recommendations than non-hybrid ones. Our approach to hybrid recommenders is the use of prediction strategies t...
Mark van Setten, Mettina Veenstra, Anton Nijholt, ...
d Abstract] Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger Carnegie Mellon University This work explores the applicatio...
Abstract. This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building vis...
Jan Petzold, Andreas Pietzowski, Faruk Bagci, Wolf...
Mining data streams is important in both science and commerce. Two major challenges are (1) the data may grow without limit so that it is difficult to retain a long history; and (...
Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and diffic...
We deploy a novel Reinforcement Learning optimization technique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction inform...
Abstract. Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of cla...
Mobile ad hoc networks (MANETs) are a very promising next generation networks. Prediction is an essential aspect in the deployment of MANETs. Current work mainly focuses on mobili...
This paper addresses one of the fundamental problems encountered in performance prediction for object recognition. In particular we address the problems related to estimation of s...