Lectures on the Nearest Neighbor Method

• Main Authors: Biau, Gérard (Author), Devroye, Luc (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Edition: 1st ed. 2015.
• Series: Springer Series in the Data Sciences,
• Subjects: Mathematics.; Pattern recognition.; Probabilities.; Statistics.; Probability Theory and Stochastic Processes.; Pattern Recognition.; Statistics and Computing/Statistics Programs.

 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. 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).   . 

 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.