Retrieval performance can often be improved 
significantly by using a number of different retrieval
algorithms and combining the results, in contrast to
using just a single retrieval algorithm.
This is because different retrieval algorithms,
or retrieval {\em experts}, often emphasize different
document and query features when determining relevance
and therefore retrieve different sets of documents.
However, it is unclear how the different experts
are to be combined, in general,
to yield a superior overall estimate.
We propose a method by which the relevance estimates
made by different experts can be automatically combined to
result in superior retrieval performance.  
We apply the method to two expert combination tasks.
The applications demonstrate that the method
can identify high performance combinations of
experts and also is a novel means for determining
the combined effectiveness of experts.