The notion of ensembles of models leading to better outcomes is widespread in the Data Science and Machine Learning communities. For example, the widely read text 'The Elements of Statistical Learning' by Hastie, Tibshirani and Friedman, devotes space to the general notion of model averaging (section 8.7-8.8) with further discussion of boosting in relation to trees (e.g. chapter 10). Hence, it is common to see examples of ensembled models in the wild, using any conceivable algorithm as the base, even to the point of effectively seeing ensembles of ensembles.
Specifically with respect to Support Vector Machines, there are at least a few attempts at improving SVM performance by using ensembles. A recent example is EnsemblesSVM (Claesen,De Smet, Suykens, De Moor - see homes.esat.kuleuven.be/~claesenm/ensemblesvm/). The authors promise that this algorithm will lead to better training times by ensembling lightly trained SVMs, and distribute a free implementation via the website above.