Spectral Partitioning for Boundary Estimation

We propose a spectral technique for analysing intermediate feature space of multiple classifier decisions, which enables a separable subset of patterns to be extracted. The method is applied to finding a set of patterns that are inconsistently classified, a random subset of which is left out of the training set of each expert in a multiple classifier framework