TY  - BOOK
AU  - Biau,Gérard
AU  - Devroye,Luc
ED  - SpringerLink (Online service)
TI  - Lectures on the Nearest Neighbor Method
T2  - Springer Series in the Data Sciences,
SN  - 9783319253886
AV  - QA273.A1-274.9 
U1  - 519.2 23
PY  - 2015///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - mathematics
KW  - Pattern Recognition
KW  - Probabilities
KW  - Statistics
KW  - Mathematics
KW  - Probability Theory and Stochastic Processes
KW  - Statistics and Computing/Statistics Programs
N1  - 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
N2  - 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).   
UR  - http://dx.doi.org/10.1007/978-3-319-25388-6
ER  -