Evaluating Learning Algorithms :A Classification Perspective

4.00 ( 8 Ratings by Goodreads)
Evaluating Learning Algorithms

Evaluating Learning Algorithms :A Classification Perspective

(Author) (Author)
4.00 (8 Ratings by Goodreads)
hardback
Published: 17 January, 2011
Standard worldwide delivery by Tue, July 7 - Fri, July 10
Order within 0
Condition: NEW
$190.38
Price includes shipping
Available 20+ in stock
- +
Add to basket Wishlist This
FREE Returns within 30 days
More Details

Popular Books by Nathalie Japkowicz

View All Books by Nathalie Japkowicz
Evaluating Learning Algorithms
Evaluating Learning Algorithms
Nathalie Japkowicz
$76.35
New
Add to basket
Machine Learning Evaluation
Machine Learning Evaluation
Nathalie Japkowicz
$79.23 $80.03
New
Add to basket
Big Data Analysis: New Algorithms for a New Society
Big Data Analysis: New Algorithms for a New Society
Nathalie Japkowicz
$222.93
New
Add to basket
Big Data Analysis: New Algorithms for a New Society
Big Data Analysis: New Algorithms for a New Society
Nathalie Japkowicz
$184.72 $200.09
New
Add to basket
Foundations of Intelligent Systems
Foundations of Intelligent Systems
Nathalie Japkowicz
$71.76
New
Add to basket
Discovery Science
Discovery Science
Nathalie Japkowicz
$70.70
New
Add to basket
Advances in Artificial Intelligence
Advances in Artificial Intelligence
Nathalie Japkowicz
$70.40
New
Add to basket

Description

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
See more

More Details

Type Book
ISBN13 9780521196000
ISBN10 0521196000
Number Of Pages 424
Item Weight 720 g
Product Dimensions 164 x 241 x 25 mm
Publisher / Reseller Cambridge University Press
Format hardback
See More +

Media Reviews

"This treasure-trove of a book covers the important topic of performance evaluation of machine learning algorithms in a very comprehensive and lucid fashion. As Japkowicz and Shah point out, performance evaluation is too often a formulaic affair in machine learning, with scant appreciation of the appropriateness of the evaluation methods used or the interpretation of the results obtained. This book makes significant steps in rectifying this situation by providing a reasoned catalogue of evaluation measures and methods, written specifically for a machine learning audience and accompanied by concrete machine learning examples and implementations in R. This is truly a book to be savoured by machine learning professionals, and required reading for Ph.D students." Peter A. Flach, University of Bristol
"This book has the merit of organizing most of the material about the evaluation of learning algorithms into a homogeneous description, covering both theoretical aspects and pragmatic issues. It is a useful resource for researchers in machine learning, and provides adequate material for graduate courses in machine learning and related fields." Corrado Mencar, Computing Reviews

Show more

Author's Bio

Nathalie Japkowicz is Professor of Computer Science at American University. She is a former assistant professor at Dalhousie University and lecturer at Ohio State University. Japkowicz co-organized numerous workshops on classifier evaluation and the class imbalance problem at AAAI and ICML. She has published many articles in peer-reviewed journals and conference proceedings. Mohak Shah is an AI and technology executive with extensive experience in bringing data and AI products to market. He has held several senior leadership roles in large enterprises and startups driving both large-scale AI transformation initiatives and zero-to-one product journeys. He is the founder and Managing Director of Praescivi Advisors, a strategic AI advisory practice. As a research scientist, Mohak has published extensively in theoretical and applied machine learning areas.

Show more