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Osteraktion: Bis zum 30.4.2025 von 20% Rabatt auf folgende Produkte profitieren. Code: NEST25
Inhalt
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.
Bibliografische Angaben
März 2025, 348 Seiten, Chapman & Hall/CRC Machine Learning & Pattern Recognition, Englisch
Taylor and Francis
978-1-032-96060-9
Inhaltsverzeichnis
Schlagworte
Inhalt
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.
Bibliografische Angaben
März 2025, 348 Seiten, Chapman & Hall/CRC Machine Learning & Pattern Recognition, Englisch
Taylor and Francis
978-1-032-96060-9