By Ian H. Witten
Data Mining: useful computing device studying instruments and strategies, Fourth variation, offers an intensive grounding in desktop studying strategies, in addition to sensible recommendation on utilising those instruments and methods in real-world facts mining events. This hugely expected fourth variation of the main acclaimed paintings on facts mining and laptop studying teaches readers every thing they should recognize to get going, from getting ready inputs, studying outputs, comparing effects, to the algorithmic equipment on the center of winning information mining approaches.
Extensive updates mirror the technical alterations and modernizations that experience taken position within the box because the final variation, together with titanic new chapters on probabilistic tools and on deep studying. Accompanying the booklet is a brand new model of the preferred WEKA computer studying software program from the college of Waikato. Authors Witten, Frank, corridor, and friend contain state-of-the-art ideas coupled with the tools on the cutting edge of latest research.
Please stopover at the booklet spouse web site at http://www.cs.waikato.ac.nz/ml/weka/book.html
- Powerpoint slides for Chapters 1-12. it is a very entire instructing source, with many PPT slides overlaying each one bankruptcy of the book
- Online Appendix at the Weka workbench; back a truly entire studying relief for the open resource software program that is going with the book
- Table of contents, highlighting the numerous new sections within the 4th version, in addition to studies of the first variation, errata, etc.
- Provides a radical grounding in laptop studying options, in addition to useful suggestion on using the instruments and methods to info mining projects
- Presents concrete tips and strategies for functionality development that paintings through reworking the enter or output in laptop studying methods
- Includes a downloadable WEKA software program toolkit, a finished number of laptop studying algorithms for information mining tasks-in an easy-to-use interactive interface
- Includes open-access on-line classes that introduce functional functions of the cloth within the book
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Extra resources for Data Mining: Practical Machine Learning Tools and Techniques
We will answer these questions by way of illustration rather than by attempting formal, and ultimately sterile, definitions. There will be plenty of examples later in this chapter, but let’s examine one right now to get a feeling for what we’re talking about. 1. This gives the conditions under which an optician might want to prescribe soft contact lenses, hard contact lenses, or no contact lenses at all; we will say more about what the individual features mean later. Each line of the table is one of the examples.
This gives the conditions under which an optician might want to prescribe soft contact lenses, hard contact lenses, or no contact lenses at all; we will say more about what the individual features mean later. Each line of the table is one of the examples. 1 The Contact Lens Data Age Spectacle Prescription Astigmatism Tear Production Rate Recommended Lenses Young Myope No Reduced None Young Myope No Normal Soft Young Myope Yes Reduced None Young Myope Yes Normal Hard Young Hypermetrope No Reduced None Young Hypermetrope No Normal Soft Young Hypermetrope Yes Reduced None Young Hypermetrope Yes Normal Hard Prepresbyopic Myope No Reduced None Prepresbyopic Myope No Normal Soft Prepresbyopic Myope Yes Reduced None Prepresbyopic Myope Yes Normal Hard Prepresbyopic Hypermetrope No Reduced None Prepresbyopic Hypermetrope No Normal Soft Prepresbyopic Hypermetrope Yes Reduced None Prepresbyopic Hypermetrope Yes Normal None Presbyopic Myope No Reduced None Presbyopic Myope No Normal None Presbyopic Myope Yes Reduced None Presbyopic Myope Yes Normal Hard Presbyopic Hypermetrope No Reduced None Presbyopic Hypermetrope No Normal Soft Presbyopic Hypermetrope Yes Reduced None Presbyopic Hypermetrope Yes Normal None If tear production rate=reduced then recommendation=noneOtherwise, if age=young and astigmatic=no then recommendation=softStructural descriptions need not necessarily be couched as rules such as these.
Since the first edition of the book the WEKA team has expanded considerably: so many people have contributed that it is impossible to acknowledge everyone properly. We are grateful to Chris Beckham, for contributing several packages to WEKA, Remco Bouckaert for his Bayes net package and many other contributions, Lin Dong for her implementations of multi-instance learning methods, Dale Fletcher for many database-related aspects, Kurt Driessens for his implementation of Gaussian process regression, James Foulds for his work on multi-instance filtering, Anna Huang for information bottleneck clustering, Martin Gütlein for his work on feature selection, Kathryn Hempstalk for her one-class classifier, Ashraf Kibriya and Richard Kirkby for contributions far too numerous to list, Nikhil Kishore for his implementation of elastic net regression, Niels Landwehr for logistic model trees, Chi-Chung Lau for creating all the icons for the Knowledge Flow interface, Abdelaziz Mahoui for the implementation of K*, Jonathan Miles for his implementation of kernel filtering, Stefan Mutter for association rule mining, Malcolm Ware for numerous miscellaneous contributions, Haijian Shi for his implementations of tree learners, Marc Sumner for his work on speeding up logistic model trees, Tony Voyle for least-median-of-squares regression, Yong Wang for Pace regression and the original implementation of M5′, Benjamin Weber for his great unification of WEKA parsing modules, and Xin Xu for his multi-instance learning package, JRip, logistic regression and many other contributions.
Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten