Semi-Supervised Learning with Cover Trees
نویسندگان
چکیده
Semi-supervised learning (SSL) has emerged in recent years as an important tool to tackle large amounts of data. Generally in large-data scenarios, one finds that the ratio of unlabeled to labeled data is very high, and that annotating and labeling data for training a learning algorithm is often resource-demanding and expensive. This issue makes semi-supervised techniques a natural way to explore, understand, and learn from these large datasets [14].
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