Use feedback or reinforcement in machine learning?
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 predict the class labels for new instances.
I am wondering if it is possible to introduce some feedback mechanism into the paradigm. In control theory, introducing a feedback loop is an effective way to improve system performance.
Currently a straight forward approach on my mind is, first we start with a initial set of instances and train a model with them. Then each time the model makes a wrong prediction, we add the wrong instance into the training set. This is different from blindly enlarge the training set because it is more targeting. This can be seen as some kind of negative feedback in the language of control theory.
Is there any research going on with the feedback approach? Could anyone shed some light?
Answer
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.