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Ensemble Methods

- Foundations and Algorithms
Af: Zhi-Hua Zhou Engelsk Hardback

Ensemble Methods

- Foundations and Algorithms
Af: Zhi-Hua Zhou Engelsk Hardback
Tjek vores konkurrenters priser

Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, for example, the fundamental question of why AdaBoost seems resistant to overfitting gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., isolation forest in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning.

Third, ensemble mechanisms have also been found helpful in emerging areas such as deep learning and online learning. This edition expands on the previous one with additional content to reflect the significant advances in the field, and is written in a concise but comprehensive style to be approachable to readers new to the subject.

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Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, for example, the fundamental question of why AdaBoost seems resistant to overfitting gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., isolation forest in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning.

Third, ensemble mechanisms have also been found helpful in emerging areas such as deep learning and online learning. This edition expands on the previous one with additional content to reflect the significant advances in the field, and is written in a concise but comprehensive style to be approachable to readers new to the subject.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 348
ISBN-13: 9781032960609
Indbinding: Hardback
Udgave: 2
ISBN-10: 1032960604
Kategori: Automatisk styrings- & reguleringsteknik
Udg. Dato: 9 mar 2025
Længde: 27mm
Bredde: 242mm
Højde: 163mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 9 mar 2025
Forfatter(e): Zhi-Hua Zhou
Forfatter(e) Zhi-Hua Zhou

Kategori Automatisk styrings- & reguleringsteknik

ISBN-13 9781032960609

Sprog Engelsk

Indbinding Hardback

Sider 348

Udgave 2

Længde 27mm

Bredde 242mm

Højde 163mm

Udg. Dato 9 mar 2025

Oplagsdato 9 mar 2025

Forlag Taylor & Francis Ltd

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