Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. Whil...
This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...
Huma Lodhi, Grigoris J. Karakoulas, John Shawe-Tay...
a This paper presents a novel Adaptive Dynamic Gridbased Data Distribution Management (DDM) scheme, which we refer to as ADGB. The main objective of our protocol is to optimize DDM...
Azzedine Boukerche, YunFeng Gu, Regina Borges de A...
Data Grids provide geographically distributed resources for large-scale data-intensive applications that generate large data sets. However, ensuring efficient access to such huge...
Houda Lamehamedi, Zujun Shentu, Boleslaw K. Szyman...
CBR applications running in real domains can easily reach thousands of cases, which are stored in the case library. Retrieval times can increase greatly if the retrieval algorithm ...
Paulo Gomes, Francisco C. Pereira, Paulo Paiva, Nu...