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...
| Published in: | Springer eBooks |
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| Main Authors: | , |
| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edition: | 1st ed. 2015. |
| Series: | Springer Series in the Data Sciences,
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1007/978-3-319-25388-6 Перейти в каталог НБ ТГУ |
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| 245 | 1 | 0 | |a Lectures on the Nearest Neighbor Method |h electronic resource |c by Gérard Biau, Luc Devroye. |
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| 505 | 0 | |a 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. | |
| 520 | |a 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). . | ||
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