Uwe Aickelin

Also published under:U. Aickelin

Affiliation

School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

Topic

Excess Risk,Faster Rate,Generalization Error,Learning Algorithms,Learning Problem,Loss Function,Mutual Information,Upper Bound,Absolutely Continuous,Class Label Of Sample,Class Labels,Classification Accuracy,Classification Algorithms,Classification Performance,Classifier Training,Cognitive Data,Collaborative Decision,Collaborative Decision-making,Competition Theory,Competitive Game,Computation Time,Conditional Mutual Information,Convergence Rate,Correlation-based Methods,Curse Of Dimensionality,Data Distribution,Data Fusion,Data Generation,Data Preparation Steps,Data Sources,Dataset Characteristics,Decision Tree,Decision Uncertainty,Decision-making Process,Decision-making System,Deep Learning,Dependent Variable,Diagnostic Methods,Discrete Random Variable,Domain Adaptation,Empirical Risk,Empirical Risk Minimization,Experimental Features,Expert Decision,Extensive Experiments,Feature Ranking,Feature Scores,Feature Selection Methods,Feature Selection Process,Feature Subset,

Biography

Uwe Aickelin received the Ph.D. degree from the University of Wales, U.K. He is currently a Professor and the Head of the School of Computing and Information Systems, The University of Melbourne. His current research interests include artificial intelligence (modeling and simulation), data mining and machine learning (robustness and uncertainty), decision support and optimization (medicine and digital economy), and health informatics (electronic healthcare records). He is an Associate Editor of the IEEE Transactions on Evolutionary Computation.