Reliable detection of sleep positions is essential for the development of technical aids for patients with position-dependent sleep-related breathing disorders. We compare personalized and generalizable sleeping position classifiers using unobtrusive eight-channel pressure-sensing mats. Data of six male patients with confirmed position-dependent sleep apnea was recorded during three subsequent nights. Personalized position classifiers trained using leave-one-night-out cross-validation on average reached an F1-score of 61.3% for supine/non-supine and an F1-score of 46.2% for supine/lateral-left/lateral-right classification. The generalizable classifiers reached average F1-scores of 62.1% and 49.1% for supine/non-supine and supine/lateral-left/lateral-right classification, respectively. In-bed presence ('bed occupancy') could be detected with an average F1-score of 98.1%. This work shows that personalized sleep-position classifiers trained with data from two nights have comparable performance to classifiers trained with large interpatient datasets. Simple eight-channel sensor mattresses can be used to accurately detect in-bed presence required for closed-loop systems but their use to classify sleep-positions is limited.