Sample Complexity of Nonparametric Semi-Supervised Learning

neural information processing systems, Volume abs/1809.03073, 2018.

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Abstract:

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

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