has been added to your Cart Since ensemble learning is very crucial to building practically useful model, I highly recommend this book to anyone who is interested in machine learning and data mining. As a business/data analyst and a machine learning PhD student, I found this book is a great read for people interested in ensemble methods from different perspectives - industrial and research. The pro- 1 f2 Ensemble Methods: Foundations and Algorithms cess of generating models from data is called learning or training, which is accomplished by a learning algorithm.

This book is very useful for researches to understanding the essence of ensemble learning.After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in.After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. I learned a lot reading it! This is mainly based on their generalization ability, which is often much stronger than that of simple/base learners.

""While the book is rather written for a machine learning and pattern recognition audience, the terminology is well explained and therefore also easily understandable for readers from other areas. This book is good for new users and also for the expert users it is good for extend the work from this book. Routledge & CRC Press eBooks are available through VitalSource. Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning series) ""This is a timely book. I think this book is well-written,and Prof. Zhou is famous for his ensemble works. For both formats the functionality available will depend on how you access the ebook (via Bookshelf Online in your browser or via the Bookshelf app on your PC or mobile device).An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. Ensemble Methods: A state-of-the-art book on a hot topic in academia and industry In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading.New and updated guide about Ai, Machines and Algorithms that influence our life. Ensemble Methods: Foundations and Algorithms (Chapman & Hall/Crc Machine Learnig & Pattern Recognition) As a researcher, I really enjoyed reading the "Diversity" and the "Ensemble pruning" chapters.

""While the book is rather written for a machine learning and pattern recognition audience, the terminology is well explained and therefore also easily understandable for readers from other areas. An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. The learned model can be called a hypothesis, and in this book it is also called a learner. This book will teach you all the advanced methods related to machine learning.What is the difference between AI, machine learning, and data analytics? Please try again. I learned a lot reading it! Therefore, the book will become a helpful tool for practitioners working in the field of machine learning or pattern recognition as well as for students of engineering or computer sciences at the graduate and postgraduate level. Which jobs AI will replace, which jobs are safe from the data revolution? I heartily recommend the book!

""Professor Zhou’s book is a comprehensive introduction to ensemble methods in machine learning. I heartily recommend this book! You can follow some related papers as suggested in the book to further investigate some topics. This shopping feature will continue to load items when the Enter key is pressed. In general the book is well structured and written and presents nicely the different ideas and approaches for combining single learners as well as their strengths and limitations.