Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions

Seni, Giovanni; Elder, John

ISBN 10: 1608452840 / ISBN 13: 9781608452842

Verlag: Morgan & Claypool, 2010

May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.66. Bestandsnummer des Verkäufers G1608452840I4N00

Inhaltsangabe:

This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. The practical implementations of ensemble methods are enormous. Most current implementations of them are quite primitive and this book will definitely raise the state of the art. Giovanni Seni's thorough mastery of the cutting-edge research and John Elder's practical experience have combined to make an extremely readable and useful book. - from Foreword 1 by Jaffray Woodriff

Reseña del editor: This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. The practical implementations of ensemble methods are enormous. Most current implementations of them are quite primitive and this book will definitely raise the state of the art. Giovanni Seni's thorough mastery of the cutting-edge research and John Elder's practical experience have combined to make an extremely readable and useful book. - from Foreword 1 by Jaffray Woodriff

Bibliografische Details

Titel: Ensemble Methods in Data Mining: Improving ...
Verlag: Morgan & Claypool
Erscheinungsdatum: 2010
Einband: Paperback
Zustand: Very Good
Zustand des Schutzumschlags: No Jacket