## Saturday, August 27, 2016

### AdaBoost 9: Boost by majority afterthought

After some time I've realized that all this monkeying with the conditional probabilities in the Bayesian table is not necessary. You can just throw away a whole training case or a part of it and continue like nothing happened, the probabilities should stay consistent anyway. After all, the point of adjusting the weights after each round opposite to how the run-time weights would be changed is to give each training case an equal chance. But if we don't want to give some training case an equal chance then there is no point in treating it equally, an ignored training case can be simply ignored.

Another thought is that it looks like the two-pass approach can be used to find what training cases to throw away in a dynamic way. We can do it by splitting the set of available cases randomly in half. Then use one half for the first pass of training of N rounds and remember the weights throughout it. Then test the effectiveness of this training on the second half of the cases. But not just run the N rounds in the test. Instead, keep the test results for using only 1 round, 2 rounds, 3 rounds, and so on all the way to the N rounds. Then see the number of rounds on which the test did best, say K rounds. Going back to the training weights, we can find, what training cases were not getting guessed well at K rounds. We can mark them as outliers. Then repeat the same thing swapping the two halves, and find the outliers in the second half. Then throw away the outliers and do the second pass of training on the rest of the cases.