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Books on the topic 'Nearest neighbor analysis (Statistics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

A spatial analysis of artifact distribution on a Boreal forest archaeological site. Edmonton, Alta: Alberta Culture, 1985.

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2

Weber, Roger. Similarity search in high dimensional vector spaces. Berlin: Aka, 2001.

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3

Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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4

Die Verteilung von Steinartefakten in Grabungsflächen: Ein Modell zur Organisation alt- und mittelsteinzeitlicher Siedlungsplätze. Tübingen: Archaeologica Venatoria, 1985.

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5

Manolopoulos, Yannis, and Apostolos N. N. Papadopoulos. Nearest Neighbor Search : : A Database Perspective. Springer, 2010.

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6

V, Dasarathy Belur, ed. Nearest neighbor (NN) norms: Nn pattern classification techniques. Los Alamitos, Calif: IEEE Computer Society Press, 1991.

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7

Papadopoulos, Apostolos N., and Yannis Manolopoulos. Nearest Neighbor Search : : A Database Perspective. Springer London, Limited, 2006.

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8

Dasarathy, Belur V. Nearest Neighbor: Pattern Classification Techniques (Nn Norms : Nn Pattern Classification Techniques). Ieee Computer Society, 1990.

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9

Flores-Roux, Ernesto M. Estimation of the nearest neighbor distribution for spatial point processes. 1993.

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10

Dietterich, Thomas G., Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk, and Michael I. Jordan. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, 2006.

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11

Nearest-neighbor methods in learning and vision: Theory and practice. Cambridge, MA: MIT Press, 2006.

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12

Dietterich, Thomas G., Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk, and Michael I. Jordan. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, 2006.

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13

Shakhnarovich, Gregory, Trevor Darrell, and Piotr Indyk. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, 2018.

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14

Nearest Neighbor Search: A Database Perspective (Series in Computer Science). Springer, 2004.

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15

Bhatti, Aman Ullah. Application of geostatistics and nearest neighbor analysis methods to statistical analysis of field experiments. 1990.

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16

Krämer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer Berlin / Heidelberg, 2013.

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17

(Editor), Gregory Shakhnarovich, Trevor Darrell (Editor), and Piotr Indyk (Editor), eds. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). The MIT Press, 2006.

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18

Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Springer, 2016.

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19

Devroye, Luc, and Gérard Biau. Lectures on the Nearest Neighbor Method. Springer, 2015.

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20

Devroye, Luc, and Gérard Biau. Lectures on the Nearest Neighbor Method. Springer, 2015.

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21

Devroye, Luc, and Gérard Biau. Lectures on the Nearest Neighbor Method. Springer, 2019.

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22

Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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Abstract:

This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.

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