How to control the precision vs. recall rate of a classifier?

The ML methods in OpenCV usually give an overall balanced error rate for both positive and negative samples. For some applications, a very high recall rate with medium precision is needed. In such case, what can be done? Your ideas and suggestions are highly welcome.