Lectures on the Nearest Neighbor Method
This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzi...
Main Authors: | , |
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Format: | eBook |
Language: | English |
Published: |
Cham
Springer International Publishing
2015, 2015
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Edition: | 1st ed. 2015 |
Series: | Springer Series in the Data Sciences
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Subjects: | |
Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Part I: Density Estimation
- Order Statistics and Nearest Neighbors
- The Expected Nearest Neighbor Distance
- The k-nearest Neighbor Density Estimate
- Uniform Consistency
- Weighted k-nearest neighbor density estimates.- Local Behavior
- Entropy Estimation
- Part II: Regression Estimation
- The Nearest Neighbor Regression Function Estimate
- The 1-nearest Neighbor Regression Function Estimate
- LP-consistency and Stone's Theorem
- Pointwise Consistency
- Uniform Consistency
- Advanced Properties of Uniform Order Statistics
- Rates of Convergence
- Regression: The Noisless Case
- The Choice of a Nearest Neighbor Estimate
- Part III: Supervised Classification
- Basics of Classification
- The 1-nearest Neighbor Classification Rule
- The Nearest Neighbor Classification Rule. Appendix
- Index