  We present a principled framework to address resource allocation for
realizing boosting algorithms on substrates with communication or computation
noise. Boosting classifiers (e.g., AdaBoost) make a final decision via a
weighted vote from the outputs of many base classifiers (weak classifiers).
Suppose that the base classifiers' outputs are noisy or communicated over noisy
channels; these noisy outputs will degrade the final classification accuracy.
We show that this degradation can be effectively reduced by allocating more
system resources for more important base classifiers. We formulate resource
optimization problems in terms of importance metrics for boosting. Moreover, we
show that the optimized noisy boosting classifiers can be more robust than
bagging for the noise during inference (test stage). We provide numerical
evidence to demonstrate the benefits of our approach.
