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Classification of Biomedical Data through Model-Based Spatial Averaging
Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05)
Ensemble learning is frequently used to reduce classification error. The more popular techniques draw multiple samples from the training data and employ a voting procedure to aggregate the decisions of the classifiers constructed from those samples. In practice, such ensemble methods have been shown to work well and improve accuracy. Here we present a meta-learning strategy that combines the decisions of classifiers constructed from spatial models taken at multiple resolutions. By varying thedoi:10.1109/bibe.2005.16 dblp:conf/bibe/MarsoloPTB05 fatcat:jslkxpnnzvdc7b2yfe3bl54tlq