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Machine learning
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=== Semi-supervised learning === {{Main|Semi-supervised learning}} Semi-supervised learning falls between [[unsupervised learning]] (without any labelled training data) and [[supervised learning]] (with completely labelled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labelled data, can produce a considerable improvement in learning accuracy. In [[Weak supervision|weakly supervised learning]], the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.<ref>{{Cite web|url=https://hazyresearch.github.io/snorkel/blog/ws_blog_post.html|title=Weak Supervision: The New Programming Paradigm for Machine Learning|author1=Alex Ratner|author2=Stephen Bach|author3=Paroma Varma|author4=Chris|others=referencing work by many other members of Hazy Research|website=hazyresearch.github.io|access-date=6 June 2019|archive-date=6 June 2019|archive-url=https://web.archive.org/web/20190606043931/https://hazyresearch.github.io/snorkel/blog/ws_blog_post.html}}</ref>
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