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Encyclopedia of Machine Learning

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description Encyclopedia of Machine Learning Overview

The Encyclopedia of Machine Learning is a comprehensive reference work published by Springer in 2011 and edited by Claude Sammut and Geoffrey I. Webb. It contains hundreds of entries detailing the algorithms, concepts, and historical developments within the field of machine learning and data mining. The text is designed for students, researchers, and practitioners seeking detailed explanations of foundational and advanced topics in artificial intelligence and computational statistics.

help Encyclopedia of Machine Learning FAQ

Who edited the Encyclopedia of Machine Learning published by Springer?

The Encyclopedia of Machine Learning was edited by Claude Sammut and Geoffrey I. Webb and published by Springer in 2011. Sammut and Webb are established researchers in the machine learning and data mining communities.

When was the Springer Encyclopedia of Machine Learning published?

The Encyclopedia of Machine Learning was published in 2011. This means it predates the explosion of deep learning and large language models that transformed the field later in the 2010s.

Does the 2011 Encyclopedia of Machine Learning cover deep learning?

The encyclopedia covers foundational machine learning concepts such as classification, clustering, reinforcement learning, and support vector machines, but its 2011 publication date means it predates the deep learning revolution. Readers should supplement it with more recent sources for topics like transformers, large language models, and modern neural architectures.

Is the Encyclopedia of Machine Learning a book or an online resource?

The Encyclopedia of Machine Learning was published by Springer as a major print reference work, and its content is also accessible through Springer's digital platforms. It is organized as an alphabetically arranged encyclopedia with entries written by contributing experts across the machine learning field.

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