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Principles and Theory for Data Mining and Machine Learning
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Variability, Information, and Prediction
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Chapter 2
Local Smoothers
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Chapter 3
Spline Smoothing
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Chapter 4
New Wave Nonparametrics
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Chapter 5
Supervised Learning: Partition Methods
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Chapter 6
Alternative Nonparametrics
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Chapter 7
Computational Comparisons
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Chapter 8
Unsupervised Learning: Clustering
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Chapter 9
Learning in High Dimensions
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Chapter 10
Variable Selection
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Chapter 11
Multiple Testing
Overall attention for this book and its chapters
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Mentioned by
twitter
23
X users
syllabi
2
institutions with syllabi
wikipedia
2
Wikipedia pages
Citations
dimensions_citation
208
Dimensions
Readers on
mendeley
179
Mendeley
Book overview
1. Variability, Information, and Prediction
2. Local Smoothers
3. Spline Smoothing
4. New Wave Nonparametrics
5. Supervised Learning: Partition Methods
6. Alternative Nonparametrics
7. Computational Comparisons
8. Unsupervised Learning: Clustering
9. Learning in High Dimensions
10. Variable Selection
11. Multiple Testing
Summary
X
Syllabi
Wikipedia
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
4
syllabi from
2
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
Florida State University
2
Unknown
Unknown
2
Media, Communications, Journalism