Authorship detection is a challenging task due to many design choices the user has to decide on. The performance highly depends on the right set of features, the amount of data, in-sample vs. out-of-sample settings, and profile- vs. instance-based approaches. So far, the variety of combinations renders off-the-shelf methods for authorship detection inappropriate. We propose a novel and generally deployable method that does not share these limitations. We treat authorship attribution as an anomaly detection problem where author regions are learned in feature space. The choice of the right feature space for a given task is identified automatically by representing the optimal solution as a linear mixture of multiple kernel functions (MKL). Our approach allows to include labelled as well as unlabelled examples to remedy the in-sample and out-of-sample problems. Empirically, we observe our proposed novel technique either to be better or on par with baseline competitors. However, our method relieves the user from critical design choices (e.g., feature set) and can therefore be used as an off-the-shelf method for authorship attribution.