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Semi-Supervised Learning: Computer Science -

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2026-05-28
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In computer science, semi-supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled data for training - typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervise ... Pilns apraksts

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In computer science, semi-supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled data for training - typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. The cost associated with the labeling process thus may render a fully labeled training set infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semi-supervised learning can be of great practical value.

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Izdevējs OmniScriptum
Izlaides gads 2026
Vāka tips Mīkstais vāks
EAN 9786138754633
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116,87 € 166,96 €