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, which mixes aspects of supervised and unsupervised methods. This technique makes use of a little level of labeled data and a bigger quantity of unlabeled data, thereby enhancing learning accuracy whilst reducing the necessity for labeled data, which can be time and labor intensive to acquire.
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