It should be noted that--contrary to boosting, where the individual runs depend on each other and have to be performed in succession--pairwise classification can be easily parallelized by assigning the binary classification problems to different processors, as already noted by Friedman (1996) and Lu and Ito (1999). As each binary task will be smaller than the original task, the total training time of a multi-class problem of size n will be significantly below that of a binary problem of the same size, if each binary classifier can be trained on a separate processor. Naturally, a parallel implementation would also provide a trivial solution to the problem with classification efficiency discussed above.