Evaluating Learning Algorithms : A Classification Perspective / by N. Japkowicz and Mohak. Shah. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 2011Description: xvi, 406pISBN:- 9780511921803
- 006.31Â J271E
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 006.31 J271E (Browse shelf(Opens below)) | Available (e-Book For Access) | Platform : Cambridge Core | EB0371 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
006.310 151 81 M729A Algorithmic Aspects of Machine Learning | 006.31 Al74M Introduction to Machine Learning | 006.31 B398S Scaling Up Machine Learning : Parallel and Distributed Approaches | 006.31 J271E Evaluating Learning Algorithms : A Classification Perspective | 006.31 J774A Adversarial Machine Learning | 006.31 M954M Machine Learning : A Probabilistic Perspective | 006.31 N329I An Introduction to Support Vector Machines and Other Kernel - Based Learning Methods |
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.
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