Boosting is the general term for improving a initially poor or weak solutions and making them better. For example, one can take a number of different classifiers, each of which is not very accurate indivdiually, and combine them by giving each a "vote" in the final outcome; this is often far more accurate than any single classifier on its own. A different form of boosting can be to identify training items that are consistenly classified poorly and adding weight to them during successive training rounds.
Used in Chap. 8: pages 108, 109; Chap. 16: page 247; Chap. 22: pages 351, 352