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Books on the topic 'Kernel Hilbert Spaces'

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Author: Grafiati
Published: 6 September 2023

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1

Berlinet, Alain, and Christine Thomas-Agnan. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9096-9.

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2

Christine, Thomas-Agnan, ed. Reproducing kernel Hilbert spaces in probability and statistics. Boston: Kluwer Academic, 2004.

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3

Dym, H. J contractive matrix functions, reproducing kernel Hilbert spaces and interpolation. Providence, R.I: Published for the Conference Board of the Mathematical Sciences by the American Mathematical Society, 1989.

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4

Pereverzyev, Sergei. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98316-1.

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5

Minggen, Cui, and Lin Yingzhen, eds. Nonlinear numerical analysis in the reproducing Kernel space. Hauppauge, N.Y: Nova Science Publishers, 2008.

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6

Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Hoboken, N.J: Wiley, 2010.

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7

Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Hoboken, N.J: Wiley, 2010.

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8

Príncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Hoboken, N.J: Wiley, 2010.

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9

E, Fennell Robert, and Minton Roland B. 1956-, eds. Structured hereditary systems. New York: Marcel Dekker, 1987.

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10

Christensen, Jens Gerlach. Trends in harmonic analysis and its applications: AMS special session on harmonic analysis and its applications : March 29-30, 2014, University of Maryland, Baltimore County, Baltimore, MD. Providence, Rhode Island: American Mathematical Society, 2015.

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11

Manton, Jonathan H., and Pierre-Olivier Amblard. Primer on Reproducing Kernel Hilbert Spaces. Now Publishers, 2015.

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12

Berlinet, Alain, and Christine Thomas-Agnan. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Springer, 2003.

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13

Berlinet, Alain, and Christine Thomas-Agnan. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Springer, 2011.

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14

Berlinet, Alain, and Christine Thomas-Agnan. Reproducing Kernel Hilbert Spaces in Probability and Statistics. Springer, 2012.

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15

Reproducing Kernel Hilbert Spaces In Probability and Statistics. Boston, MA: Springer US, 2004.

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16

Paulsen, Vern I., and Mrinal Raghupathi. Introduction to the Theory of Reproducing Kernel Hilbert Spaces. Cambridge University Press, 2016.

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17

Paulsen, Vern I., and Mrinal Raghupathi. Introduction to the Theory of Reproducing Kernel Hilbert Spaces. Cambridge University Press, 2016.

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18

Introduction to the Theory of Reproducing Kernel Hilbert Spaces. Cambridge University Press, 2016.

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19

Pereverzyev, Sergei. Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces. Springer International Publishing AG, 2022.

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20

(Translator), Stephen S. Wilson, ed. The Schur Algorithm, Reproducing Kernel Spaces and System Theory. American Mathematical Society, 2001.

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21

Principe, Jos, Simon Haykin, and Weifeng Liu. Kernel Adaptive Filtering. Wiley & Sons, Incorporated, John, 2010.

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22

Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2010.

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23

Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2011.

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24

Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2010.

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25

Haykin, Simon, José C. Principe, and Weifeng Liu. Kernel Adaptive Filtering: A Comprehensive Introduction. Wiley & Sons, Incorporated, John, 2011.

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26

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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