# Nonlinear Dynamic Online Classification: Looking for an Algorithm

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.

In stochastic gradient descent, you take one gradient descent step each time you get a new training example. On mini-batch, you do it each time you gather a batch of n training examples.