Use feedback or reinforcement in machine learning?

Question

I am trying to solve some classification problem. It seems many classical approaches follow a similar paradigm. That is, train a model with some training set and than use it to

Answer 1

There are two areas of research that spring to mind.

The first is Reinforcement Learning. This is an online learning paradigm that allows you to get feedback and update your policy (in this instance, your classifier) as you observe the results.

The second is active learning, where the classifier gets to select examples from a pool of unclassified examples to get labelled. The key is to have the classifier choose the examples for labelling which best improve its accuracy by choosing difficult examples under the current classifier hypothesis.

Answer 2

I have used such feedback for every machine-learning project I worked on. It allows to train on less data (thus training is faster) than by selecting data randomly. The model accuracy is also improved faster than by using randomly selected training data. I'm working on image processing (computer vision) data so one other type of selection I'm doing is to add clustered false (wrong) data instead of adding every single false data. This is because I assume I will always have some fails, so my definition for positive data is when it is clustered in the same area of the image.

Answer 3

I saw this paper some time ago, which seems to be what you are looking for.

They are basically modeling classification problems as Markov decision processes and solving using the ACLA algorithm. The paper is much more detailed than what I could write here, but ultimately they are getting results that outperform the multilayer perceptron, so this looks like a pretty efficient method.