Data mining : practical machine learning tools and techniques

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 acclaime...

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Bibliographic Details
Main Authors: Witten, I. H. (Author), Frank, Eibe, (Author), Hall, Mark A. (Author), Pal, Christopher J., (Author)
Format: eBook
Language: English
Published: Cambridge, MA : Morgan Kaufmann Publisher, [2017]
Edition: Fourth edition.
Subjects:
Data mining.
elektronické knihy
electronic books
ISBN: 9780128043578
9780128042915
Physical Description: 1 online zdroj (xxxii, 621 stran) : ilustrace

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Table of contents

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100 1 |a Witten, I. H.  |q (Ian H.),  |e author. 
245 1 0 |a Data mining :  |b practical machine learning tools and techniques /  |c Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal. 
250 |a Fourth edition. 
264 1 |a Cambridge, MA :  |b Morgan Kaufmann Publisher,  |c [2017] 
300 |a 1 online zdroj (xxxii, 621 stran) :  |b ilustrace 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
504 |a Obsahuje bibliografické odkazy (stran 573-600) a index. 
505 0 |a Part I. Introduction to data mining. Chapter 1. What's it all about? -- Chapter 2. Input: concepts, instances, attributes -- Chapter 3. Output: knowledge representation -- Chapter 4. Algorithms: the basic methods -- Chapter 5. Credibility: evaluating what's been learned -- Part II. More advanced machine learning schemes. Chapter 6. Trees and rules -- Chapter 7. Extending instance-based and linear models -- Chapter 8. Data transformations -- Chapter 9. Probabilistic methods -- Chapter 10. Deep learning -- Chapter 11. Beyond supervised and unsupervised learning -- Chapter 12. Ensemble learning - Chapter 13. Moving on: applications and beyond -- Appendix A. Theoretical foundations -- Appendix B. The WEKA workbench. 
520 |a 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 bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. 
590 |a Elsevier  |b ScienceDirect All Books 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity 
650 0 |a Data mining. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Frank, Eibe,  |e author. 
700 1 |a Hall, Mark A.  |q (Mark Andrew),  |e author. 
700 1 |a Pal, Christopher J.,  |e author. 
776 0 8 |i Print version:  |z 0128042915  |z 9780128042915  |w (OCoLC)951507761 
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