Peter Flach

Machine Learning

• Gebundenes Buch

Produktbeschreibung

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 416
• Erscheinungstermin: 31. August 2018
• Englisch
• Abmessung: 260mm x 208mm x 27mm
• Gewicht: 1112g
• ISBN-13: 9781107096394
• ISBN-10: 1107096391
• Artikelnr.: 36876252

Autorenporträt

Peter Flach has more than twenty years of experience in machine learning teaching and research. He is Editor-in-Chief of Machine Learning and Program Co-Chair of the 2009 ACM Conference on Knowledge Discovery and Data Mining and the 2012 European Conference on Machine Learning and Data Mining. His research spans all aspects of machine learning, from knowledge representation and the use of logic to learn from highly structured data to the analysis and evaluation of machine learning models and methods to large-scale data mining. He is particularly known for his innovative use of Receiver Operating Characteristic (ROC) analysis for understanding and improving machine learning methods. These innovations have proved their effectiveness in a number of invited talks and tutorials and now form the backbone of this book.

Inhaltsangabe

Prologue: a machine learning sampler
1. The ingredients of machine learning
2. Binary classification and related tasks
3. Beyond binary classification
4. Concept learning
5. Tree models
6. Rule models
7. Linear models
8. Distance-based models
9. Probabilistic models
10. Features
11. In brief: model ensembles
12. In brief: machine learning experiments
Epilogue: where to go from here
Important points to remember
Bibliography
Index.