Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

Witten, Ian H.; Frank, Eibe; Hall, Mark A.; Pal, Christopher J.

ISBN 10: 0128042915 / ISBN 13: 9780128042915

Verlag: Morgan Kaufmann, 2016

Book is considered to be in good or better condition. The actual cover image may not match the stock photo. Hard cover books may show signs of wear on the spine, cover or dust jacket. Paperback book may show signs of wear on spine or cover as well as having a slight bend, curve or creasing to it. Book should have minimal to no writing inside and no highlighting. Pages should be free of tears or creasing. Stickers should not be present on cover or elsewhere, and any CD or DVD expected with the book is included. Book is not a former library copy. Bestandsnummer des Verkäufers SEWV.0128042915.G

Inhaltsangabe:

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

Über die Autorinnen und Autoren: Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography.

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now a professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.

Mark A. Hall holds a bachelor’s degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published several articles on machine learning and data mining and has refereed for conferences and journals in these areas.

Christopher J. Pal is a Canada CIFAR AI Chair and a full professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. Pal’s research interests include computer vision and pattern recognition, computational photography, natural language processing, statistical machine learning and applications to human computer interaction.

Bibliografische Details

Titel: Data Mining: Practical Machine Learning ...
Verlag: Morgan Kaufmann
Erscheinungsdatum: 2016
Einband: Softcover
Zustand: good
Auflage: 4. Auflage