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84,55 €1. Order statistics and nearest neighbors
2. The nearest neighbor distance
3. The
4. Uniform consistency
5. Weighted
6. Local behavior
7. Entropy estimation
8. The nearest neighbor regression function estimate
9. The 1-nearest neighbor regression function estimate
10. Lp Consistency and stone`s theorem
11. Pointwise consistency
12. Uniform consistency
13. Advanced properties of uniform order statistics
14. Rates of convergence
15. Regression: the noiseless case
16. The choice of a nearest neighbor estimate
17. Basics of classification
18. The nearest neighbor rule: fixed
19. The nearest neighbor rule: variable
20. Appendix
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, analyzing and developing nearest neighbor methods.
Features
• Presents a rigorous overview of nearest neighbor methods
• Many different components covered: statistical, probabilistic, combinatorial, and geometric ideas
• Extensive appendix material provided
Authors
• Gérard Biau is a professor at Université Pierre et Marie Curie (Paris).
• Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).
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