{
"item_title" : "Ensemble Methods",
"item_author" : [" Zhi-Hua Zhou "],
"item_description" : "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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Overview
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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Details
- ISBN-13: 9781032960609
- ISBN-10: 1032960604
- Publisher: CRC Press
- Publish Date: March 2025
- Dimensions: 9.21 x 6.14 x 0.81 inches
- Shipping Weight: 1.51 pounds
- Page Count: 348
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