New Book: "Evaluating Learning Algorithms: A Classification Perspective"

[Mohak Shah]

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Apologies for Cross Postings
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We would like to inform you of our latest book focusing on various aspects of performance evaluation titled "Evaluating Learning Algorithms: A Classification Perspective" published by Cambridge University Press. The book looks into the applied aspects of classifier evaluation and should be relevant especially to researchers and practitioners in data mining and machine learning. The book is now available for purchase from major publishers including Amazon, as well as directly from the publisher (some links available at the end of message). The relevant links as well as brief description are provided below:
 
Authors: Nathalie Japkowicz, University of Ottawa, and Mohak Shah, McGill University (now at Accenture Technology Labs)
 
Context: 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.
 
Review by Peter Flach, University of Bristol:
"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 
 
Links for purchase: Amazon, US: http://www.amazon.com/Evaluating-Learning-Algorithms-Classification-Perspective/dp/0521196000/ref=sr_1_1?ie=UTF8&s=books&qid=1296490420&sr=8-1
Amazon, Canada: http://www.amazon.ca/Evaluating-Learning-Algorithms-Classification-Perspective/dp/0521196000/ref=sr_1_1?ie=UTF8&s=books&qid=1296490047&sr=8-1
Amazon, UK: http://www.amazon.co.uk/Evaluating-Learning-Algorithms-Classification-Perspective/dp/0521196000/ref=sr_1_1?ie=UTF8&s=books&qid=1305646913&sr=8-1
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Mohak Shah, PhD
Accenture Technology Labs
161 N. Clark St
Chicago, IL, 60601
USA
Ph: 312-693-0439
http://www.mohakshah.com