The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), pp. 9344-9354, 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 Omega(K logK) labeled sample complexity bound...More

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