The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian kernel in a bootstrap boosting algorithm in kernel density estimation was investigated. The algorithm used the higher-orderGaussian kernel instead of the regular fixed kernels. A comparison of the scheme with existing fixed kernel methods indicated the results were better
One way of improving the performance, at least in theory, of kernel estimators of curves such as pro...
Kernel density estimation is a commonly used approach to classification. However, most of the theore...
In this paper, kernel feature selection is proposed to improve generalization performance of boostin...
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density e...
Smoothed bootstrap method is a useful method to approximates the bias of Kernel density estimation. ...
In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in ...
In the context of functional estimation, the bootstrap approach amounts to substitution of the empir...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
We propose a generalized smooth bootstrap scheme for estimating the bias By and mean square error My...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
In this work, higher order optimal window width is used to generate bootstrap kernel density likelih...
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density es...
This paper proposes a new algorithm for boosting in kernel density estimation (KDE). This algorithm ...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
Kernel density estimates are frequently used, based on a second order kernel. Thus, the bias inheren...
One way of improving the performance, at least in theory, of kernel estimators of curves such as pro...
Kernel density estimation is a commonly used approach to classification. However, most of the theore...
In this paper, kernel feature selection is proposed to improve generalization performance of boostin...
This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density e...
Smoothed bootstrap method is a useful method to approximates the bias of Kernel density estimation. ...
In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in ...
In the context of functional estimation, the bootstrap approach amounts to substitution of the empir...
We consider many kernel-based density estimators, all theoretically improving bias from O(h2), as th...
We propose a generalized smooth bootstrap scheme for estimating the bias By and mean square error My...
Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is...
In this work, higher order optimal window width is used to generate bootstrap kernel density likelih...
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density es...
This paper proposes a new algorithm for boosting in kernel density estimation (KDE). This algorithm ...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
Kernel density estimates are frequently used, based on a second order kernel. Thus, the bias inheren...
One way of improving the performance, at least in theory, of kernel estimators of curves such as pro...
Kernel density estimation is a commonly used approach to classification. However, most of the theore...
In this paper, kernel feature selection is proposed to improve generalization performance of boostin...
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