Ensemble Learning

Pattern Classification Using Ensemble Methods

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Theory, General Computing
Cover of the book Ensemble Learning by Lior Rokach, World Scientific Publishing Company
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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.
0

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

Readership: Professionals, researchers, academics, and graduate students in artificial intelligence, databases and machine learning.
0

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