  The paper describes an application of Aggregating Algorithm to the problem of
regression. It generalizes earlier results concerned with plain linear
regression to kernel techniques and presents an on-line algorithm which
performs nearly as well as any oblivious kernel predictor. The paper contains
the derivation of an estimate on the performance of this algorithm. The
estimate is then used to derive an application of the Complexity Approximation
Principle to kernel methods.
