Sample Complexity of Nonparametric Semi-Supervised Learning
neural information processing systems, Volume abs/1809.03073, 2018.
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions. Under these assumptions, we establish an Ω(KlogK) labeled sample complexity bound with...More
Full Text (Upload PDF)
PPT (Upload PPT)