The GLM model does not take into account the contribution of relationships between multiple voxels. Whereas GLM analysis methods assess whether a voxel or region's signal amplitude is higher or lower for one condition than another, newer statistical models such as multi-voxel pattern analysis (MVPA), utilize the unique contributions of multiple voxels within a voxel-population. In a typical implementation, a classifier or more basic algorithm is trained to distinguish trials for different conditions within a subset of the data. The trained model is then tested by predicting the conditions of the remaining (independent) data. This approach is most typically achieved by training and testing on different scanner sessions or runs. If the classifier is linear, then the training model is a set of weights used to scale the value in each voxel before summing them to generate a single number that determines the condition for each testing set trial. More information on training and testing classifiers is at statistical classification. Another method used the same fMRI dataset for visual object recognition in the human brain is depending on multi-voxel pattern analysis (fMRI voxels) and multi-view learning which is described in, this method used meta-heuristic search and mutual information to eliminate noisy voxels and select the significant BOLD signals.