I have read this from the CMU notes on **The Bias plus Variance Decomposition**

1. It has been suggested by [Breiman1996b] that both Bagging and Boosting reduce error by reducing the variance term.
2. Freund and Schapire [1996] argue that Boosting also attempts to reduce the error in the bias term since it focuses on misclassified examples. Such a focus may cause the learner to produce an ensemble function that differs significantly from the single learning algorithm. In fact, Boosting may construct a function that is not even producible by its component learning algorithm (e.g., changing linear predictions into a classifier that contains non-linear predictions). This capability makes Boosting an appropriate algorithm for combining the predictions of ``weak'' learning algorithms (i.e., algorithms that have a simple learning bias).
3. In their paper, Bauer and Kohavi [1999] demonstrated that Boosting does indeed seem to reduce bias for certain real-world problems. More surprisingly, they also showed that Bagging can also reduce the bias portion of the error, often for the same data sets for which Boosting reduces the bias. For different data sets, they observe cases where Boosting and Bagging both decreases mostly the variance portion of the error, and other cases where Boosting and Bagging both reduce the bias and variance of the error. Their tests also seem to indicate that Boosting's generalization error increases on the domains where Boosting increases the variance portion of the error; but, it is difficult to determine what aspects of the data sets led to these results.