Release from active learning/model selection dilemma: optimizing sample points and models at the same time
Masashi Sugiyama, H. Ogawa · 2003
In supervised learning, the selection of sample points and models is crucial for acquiring a higher level of the generalization capability. So far, the problems of active learning and model selection have been independently studied. If sample points and models are simultaneously optimized, then a higher level of the generalization capability is expected. We call this problem active learning with model selection. However, this problem can not be generally solved by simply combining existing active learning and model selection techniques because of the active learning/model selection dilemma: the model should be fixed for selecting sample points, and conversely the sample points should be fixed for selecting models. In spite of the dilemma, we show that the problem of active learning with model selection can be straightforwardly solved if there is a set of sample points that is optimal for all models in consideration. Based on the idea, we give a procedure for active learning with model selection in trigonometric polynomial models.