Results 321 to 330 of about 29,026,283 (376)
Kernel Methods for Clustering [PDF]
, 2006 Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering.
openaire +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2009
Support vector machines and kernel-based learning methods have been successful in a wide variety of applications, especially for problems in high dimensional input spaces. In this chapter we briefly outline kernel-based learning methods in relation to support vector machines and least squares support vector machines for supervised and unsupervised ...
openaire +4 more sources
Support vector machines and kernel-based learning methods have been successful in a wide variety of applications, especially for problems in high dimensional input spaces. In this chapter we briefly outline kernel-based learning methods in relation to support vector machines and least squares support vector machines for supervised and unsupervised ...
openaire +4 more sources
A reliable data-based bandwidth selection method for kernel density estimation
, 1991We present a new method for data-based selection of the bandwidth in kernel density estimation which has excellent properties. It improves on a recent procedure of Park and Marron (which itself is a good method) in various ways. First, the new method has
S. Sheather, M. C. Jones
semanticscholar +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.
openaire +2 more sources
A High-Order Kernel Method for Diffusion and Reaction-Diffusion Equations on Surfaces
Journal of Scientific Computing, 2012In this paper we present a high-order kernel method for numerically solving diffusion and reaction-diffusion partial differential equations (PDEs) on smooth, closed surfaces embedded in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage ...
E. Fuselier, G. Wright
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.
D. Comaniciu +2 more
semanticscholar +1 more source
A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.
D. Comaniciu +2 more
semanticscholar +1 more source
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
openaire +1 more source
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
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, Mallappa Kumara Swamy
openaire +2 more sources
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, Mallappa Kumara Swamy
openaire +2 more sources
Reproducing kernel particle methods
International Journal for Numerical Methods in Fluids, 1995AbstractA new continuous reproducing kernel interpolation function which explores the attractive features of the flexible time‐frequency and space‐wave number localization of a window function is developed. This method is motivated by the theory of wavelets and also has the desirable attributes of the recently proposed smooth particle hydrodynamics ...
Wing Kam Liu, Sukky Jun, Yi Fei Zhang
openaire +2 more sources
Kernel Methods in Bioinformatics
2011Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.
openaire +3 more sources