Ensemble Learning
Pattern Classification Using Ensemble Methods
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Theory, General Computing
| Author: | Lior Rokach | ISBN: | 9789811201974 |
| Publisher: | World Scientific Publishing Company | Publication: | February 27, 2019 |
| Imprint: | WSPC | Language: | English |
| Author: | Lior Rokach |
| ISBN: | 9789811201974 |
| Publisher: | World Scientific Publishing Company |
| Publication: | February 27, 2019 |
| Imprint: | WSPC |
| Language: | English |
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.
Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.
The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Contents:
- Introduction to Machine Learning
- Classification and Regression Trees
- Introduction to Ensemble Learning
- Ensemble Classification
- Gradient Boosting Machines
- Ensemble Diversity
- Ensemble Selection
- Error Correcting Output Codes
- Evaluating Ensembles of Classifiers
Readership: Professionals, researchers, academics, and graduate students in artificial intelligence, databases and machine learning.
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This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.
Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.
The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.
Contents:
- Introduction to Machine Learning
- Classification and Regression Trees
- Introduction to Ensemble Learning
- Ensemble Classification
- Gradient Boosting Machines
- Ensemble Diversity
- Ensemble Selection
- Error Correcting Output Codes
- Evaluating Ensembles of Classifiers
Readership: Professionals, researchers, academics, and graduate students in artificial intelligence, databases and machine learning.
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