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.
mars 2025, env. 348 pages, Chapman & Hall/CRC Machine Learning & Pattern Recognition, Anglais
Taylor and Francis
978-1-032-96060-9
Taylor and Francis
978-1-032-96060-9
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.
mars 2025, env. 348 pages, Chapman & Hall/CRC Machine Learning & Pattern Recognition, Anglais
Taylor and Francis
978-1-032-96060-9
Taylor and Francis
978-1-032-96060-9
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