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Study of anomaly registration detection based on multilayer kernel autoencoder extreme learning machine model. [PDF]
Sci RepGu Z +5 more
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Generalizing an outbreak cluster detection method for two groups: an application to rabies. [PDF]
R Soc Open SciHayes S +6 more
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2015
What the reader should know to understand this chapter • Notions of calculus. • Chapters 5, 6, and 7. • Although the reading of Appendix D is not mandatory, it represents an advantage for the chapter understanding.
CAMASTRA, Francesco +1 more
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What the reader should know to understand this chapter • Notions of calculus. • Chapters 5, 6, and 7. • Although the reading of Appendix D is not mandatory, it represents an advantage for the chapter understanding.
CAMASTRA, Francesco +1 more
openaire +2 more sources
2007
During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
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During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
openaire +1 more source
The Reproducing Kernel Method. II
Journal of Mathematical Physics, 1972The explicit solution of the Cauchy problem ∂N/∂t = HN by means of reproducing kernels is obtained under various forms: conformal mapping expansions, Sheffer polynomial expansion, polynomials orthogonal on a family of curves; the convergence is studied for both Szegö and Bergman kernels.
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Fully adaptive kernel‐based methods
International Journal for Numerical Methods in Engineering, 2018SummaryBy exploiting the meshless property of kernel‐based collocation methods, we propose a fully automatic numerical recipe for solving interpolation/regression and boundary value problems adaptively. The proposed algorithm is built upon a least squares collocation formulation on some quasi‐random point sets with low discrepancy.
Leevan Ling, Sung Nok Chiu
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2013
The kernel method was originally invented in Aizerman et al. (Autom. Remote Control, 25, 821–837, 1964). The key idea is to project the training set in a lower-dimensional space into a high-dimensional kernel (feature) space by means of a set of nonlinear kernel functions.
Ke-Lin Du, M. N. S. Swamy
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The kernel method was originally invented in Aizerman et al. (Autom. Remote Control, 25, 821–837, 1964). The key idea is to project the training set in a lower-dimensional space into a high-dimensional kernel (feature) space by means of a set of nonlinear kernel functions.
Ke-Lin Du, M. N. S. Swamy
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IFAC Proceedings Volumes, 2003
Abstract A disadvantage of many statistical modelling techniques is that the resulting model is extremely difficult to interpret. A number of new concepts and algorithms have been introduced by researchers to address this problem. They focus primarily on determining which inputs arc relevant in predicting the output. This work describes a transparent,
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Abstract A disadvantage of many statistical modelling techniques is that the resulting model is extremely difficult to interpret. A number of new concepts and algorithms have been introduced by researchers to address this problem. They focus primarily on determining which inputs arc relevant in predicting the output. This work describes a transparent,
openaire +1 more source