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A Probabilistic Theory of Pattern Recognition
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Introduction
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Chapter 2
The Bayes Error
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Chapter 3
Inequalities and Alternate Distance Measures
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Chapter 4
Linear Discrimination
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Chapter 5
Nearest Neighbor Rules
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Chapter 6
Consistency
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Chapter 7
Slow Rates of Convergence
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Chapter 8
Error Estimation
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Chapter 9
The Regular Histogram Rule
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Chapter 10
Kernel Rules
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Chapter 11
Consistency of the k -Nearest Neighbor Rule
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Chapter 12
Vapnik-Chervonenkis Theory
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Chapter 13
Combinatorial Aspects of Vapnik-Chervonenkis Theory
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Chapter 14
Lower Bounds for Empirical Classifier Selection
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Chapter 15
The Maximum Likelihood Principle
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Chapter 16
Parametric Classification
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Chapter 17
Generalized Linear Discrimination
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Chapter 18
Complexity Regularization
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Chapter 19
Condensed and Edited Nearest Neighbor Rules
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Chapter 20
Tree Classifiers
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Chapter 21
Data-Dependent Partitioning
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Chapter 22
Splitting the Data
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Chapter 23
The Resubstitution Estimate
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Chapter 24
Deleted Estimates of the Error Probability
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Chapter 25
Automatic Kernel Rules
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Chapter 26
Automatic Nearest Neighbor Rules
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Chapter 27
Hypercubes and Discrete Spaces
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Chapter 28
Epsilon Entropy and Totally Bounded Sets
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Chapter 29
Uniform Laws of Large Numbers
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Chapter 30
Neural Networks
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Chapter 31
Other Error Estimates
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Chapter 32
Feature Extraction
Overall attention for this book and its chapters
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Mentioned by
news
1
news outlet
twitter
10
X users
syllabi
7
institutions with syllabi
wikipedia
3
Wikipedia pages
Citations
dimensions_citation
1901
Dimensions
Readers on
mendeley
624
Mendeley
citeulike
1
CiteULike
Book overview
1. Introduction
2. The Bayes Error
3. Inequalities and Alternate Distance Measures
4. Linear Discrimination
5. Nearest Neighbor Rules
6. Consistency
7. Slow Rates of Convergence
8. Error Estimation
9. The Regular Histogram Rule
10. Kernel Rules
11. Consistency of the k -Nearest Neighbor Rule
12. Vapnik-Chervonenkis Theory
13. Combinatorial Aspects of Vapnik-Chervonenkis Theory
14. Lower Bounds for Empirical Classifier Selection
15. The Maximum Likelihood Principle
16. Parametric Classification
17. Generalized Linear Discrimination
18. Complexity Regularization
19. Condensed and Edited Nearest Neighbor Rules
20. Tree Classifiers
21. Data-Dependent Partitioning
22. Splitting the Data
23. The Resubstitution Estimate
24. Deleted Estimates of the Error Probability
25. Automatic Kernel Rules
26. Automatic Nearest Neighbor Rules
27. Hypercubes and Discrete Spaces
28. Epsilon Entropy and Totally Bounded Sets
29. Uniform Laws of Large Numbers
30. Neural Networks
31. Other Error Estimates
32. Feature Extraction
Summary
News
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
12
syllabi from
7
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
Cornell University
4
Unknown
The University of Texas at Dallas
2
Computer Science
The University of Texas at Austin
1
Unknown
Ohio State University-Main Campus
1
Computer Science
Princeton University
1
Computer Science
University of California-San Diego
1
Unknown
Unknown
2
Unknown