I don't see why not. Different classifiers have different bias/variance characteristics. If you want to increase variance and decrease bias, than boost your classifier. (This is why boosting is usually applied to simple classifiers.) But whether that will actually help depends on the characteristics of the problem and the classifier used.

I guess bagging is a different story. So far as I know bagging usually decreases variance with no bias penalty, so it is more a trade-off between variance and speed.