White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision-recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.