Semi-Supervised Learning: the Case When Unlabeled Data is Equally Useful

22 May 2020Jingge Zhu

Semi-supervised learning algorithms attempt to take advantage of relatively inexpensive unlabeled data to improve learning performance. In this work, we consider statistical models where the data distributions can be characterized by continuous parameters... (read more)

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