I have two predictors a,b that I want to use combine to classify data. a is stable, it will always produce the same prediction for the same input. b will change and probably improve in time (because it represents user feedback).

I am looking for an algorithm to combine a and b that is nonlinear and trained step by step (whenever new user feedback comes in, thus b changes) and that weights more recent events higher than those in the past, because earlier events represent an older version of b.