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Practical Tikhonov Regularized Estimators in Reproducing Kernel Hilbert Spaces for Statistical Inverse Problems
Tikhonov Regularized Estimators Reproducing Kernel Hilbert Spaces Statistical Inverse Problems
2013/6/13
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
Approximation properties of certain operator-induced norms on Hilbert spaces
L2 approximation Empirical norm Quadratic functionals Hilbert spaces with reproducing kernels Analysis of M-estimators
2011/6/20
We consider a class of operator-induced norms, acting as finite-dimensional
surrogates to the L2 norm, and study their approximation properties over
Hilbert subspaces of L2. The class includes, as a...
Minimax Robust Function Approximation in Reproduction Kernel Hilbert Spaces
RKHS Thin-Plate Splines Smoothing Splines Scattered Data Interpolation and Approximation
2010/4/30
In this paper, we present a unified approach to function approximation in reproducing kernel Hilbert spaces (RKHS) that establishes a previously unrecognized optimality property for several well-known...
K-Dimensional Coding Schemes in Hilbert Spaces
Empirical risk minimization estimation bounds K-means clus-tering and vector quantization statistical learning
2010/3/10
This paper presents a general coding method where data in
a Hilbert space are represented by finite dimensional coding vectors. The
method is based on empirical risk minimization within a certain cl...
Tightness criteria for random measures with application to the principle of conditioning in Hilbert spaces
Tightness criteria random measures with application
2009/9/23
Tightness criteria for random measures with application to the principle of conditioning in Hilbert spaces。
A roumula for the density of the norm of stable random vectors in Hilbert spaces
the density of the norm stable random vectors Hilbert spaces
2009/9/23
A roumula for the density of the norm of stable random vectors in Hilbert spaces。
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...