Of course other recent statistical learning methods, such as boosting (Freund and Schapire 1997) and support vector machines (cf. Vapnik 1995, for an introduction) can also be applied to the scope of problems we suggested for the application of random forests. The performance of these methods is within a close range with that of random forests, so that in some comparison studies random forests clearly outperform their competitors (cf., e.g., Wu, Abbott, Fishman, McMurray, Mor, Stone, Ward, Williams, and Zhao 2003), while in others they are slightly outperformed (cf., e.g., König, Malley, Pajevic, Weimar, Diener, and Ziegler 2008, for a comparison of several statistical learning methods in a medical example of moderate size, where logistic regression was also applicable).
