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Kernel methods in machine learning
Machine learning reproducing kernels support vector machines graphical models
2010/4/26
We review machine learning methods employing positive definite
kernels. These methods formulate learning and estimation problems
in a reproducing kernel Hilbert space (RKHS) of functions defined
on...
Profile-Kernel likelihood inference with diverging number of parameters
Generalized linear models varying coefficients high dimensionality asymptotic normality profile likelihood generalized likelihood ratio tests
2010/4/26
The generalized varying coefficient partially linear model with growing
number of predictors arises in many contemporary scientific endeavor. In
this paper we set foot on both theoretical and practi...
Large and moderate deviations principles for kernel estimators of the multivariate regression
Nadaraya-Watson estimator Recursive kernel estimator Large deviations principle Moderatedeviations principle
2010/4/27
In this paper, we prove large deviations principle for the
Nadaraya-Watson estimator and for the semi-recursive kernel
estimator of the regression in the multidimensional case.
Under suitable condi...
Kernel matrix regression
Kernel matrix regression kernel Hilbert space information geometry
2010/4/27
We address the problem of filling missing entries in a kernel Gram matrix, given a related full Gram matrix.We attack this problem from the viewpoint of regression, assuming that the two kernel matric...
Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces
Functional regression models Representer theorem Reproducing kernel Hilbertspace Generalized cross-validation Kernel estimate
2010/4/26
An extension of reproducing kernel Hilbert space (RKHS) theory provides a new
framework for modeling functional regression models with functional responses. The approach
only presumes a general nonl...