Indian Statistical Institute Library, Kolkata

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Library,Documentation and Information Science Division

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Ensemble Learning: Pattern Classification Using Ensemble Methods / Lior Rokach

By: Rokach, Lior [author].
Series: Machine Perception and Artificial Intelligence 85.Publisher: Singapore: World Scientific, 2019Edition: 2nd.Description: xv, 284 pages, diagrams; 23 cm.ISBN: 9789811201950.Subject(s): Machine Learning | Ensemble Learning | Artificial Intelligence | Pattern Classification | Machine PerceptionDDC classification: 006.31
Contents:
Preface 1. Introduction to machine learning 2. Classification and Regression trees 3. Introduction to Ensemble learning 4. Ensemble Classification 5. Gradient Boosting Machines 6. Ensemble Diversity 7. Ensemble Selection 8. error Correcting Output Codes 9. Evaluating Ensembles of Classifiers Bibliography Index
Summary: 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 trade offs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
006.31 R742 (Browse shelf) Available 138444
Total holds: 0

Includes bibliography and index

Preface
1. Introduction to machine learning
2. Classification and Regression trees
3. Introduction to Ensemble learning
4. Ensemble Classification
5. Gradient Boosting Machines
6. Ensemble Diversity
7. Ensemble Selection
8. error Correcting Output Codes
9. Evaluating Ensembles of Classifiers
Bibliography
Index

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 trade offs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.

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