Data Mining
Practical Machine Learning Tools and Techniques
(Sprache: Englisch)
Rev. edition of: Data mining: practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.
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Klappentext zu „Data Mining “
Rev. edition of: Data mining: practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.
Inhaltsverzeichnis zu „Data Mining “
Part I: Introduction to data mining 1. What's it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what's been learned Part II. More advanced machine learning schemes 6. Trees and rules 7. Extending instance-based and linear models 8. Data transformations 9. Probabilistic methods 10. Deep learning 11. Beyond supervised and unsupervised learning 12. Ensemble learning 13. Moving on: applications and beyond
Autoren-Porträt von Ian H. Witten, Eibe Frank, Mark A. Hall
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.
Bibliographische Angaben
- Autoren: Ian H. Witten , Eibe Frank , Mark A. Hall
- 2017, 4. Auflage, Masse: 19,1 x 23,3 cm, Kartoniert (TB), Englisch
- Verlag: Morgan Kaufmann
- ISBN-10: 0128042915
- ISBN-13: 9780128042915
Sprache:
Englisch
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