I have an existing set of data and plan to generate more data that follows the same pattern. To do this, I plan to use unsupervised learning. How can I provide feedback on the generated data and reinforce "good" and discourage "bad" results?
In other words: how can I combine reinforced and unsupervised learning? Or am I approaching this problem the wrong way?
edit: I am interested in using machine learning to generate music. It would be comparatively easy to use an unsupervised network to generate new music, but how would human graders come into play? Let's say I generate 10 samples and have a human answer good or bad, how can I improve the network? I thought of simply feeding back the "good" pieces back in, but there must be a better way.