Ensemble Methods - Zhou, Zhi-Hua
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Erscheint vorauss. 10. März 2025
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Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

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Produktbeschreibung
Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. An ensemble is significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.
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Autorenporträt
Zhi-Hua Zhou, Professor of Computer Science and Artificial Intelligence at Nanjing University, President of IJCAI trustee, Fellow of the ACM, AAAI, AAAS, IEEE, recipient of the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, CCF-ACM Artificial Intelligence Award.