The function approximation problem is to find the appropriate relationship between a dependent and independent variable(s). Function approximation algorithms generally require sufficient samples to approximate a function. Insufficient samples may cause any approximation algorithm to result in unsatisfactory predictions. To solve this problem, a function approximation algorithm called Weighted Kernel Regression (WKR), which is based on Nadaraya-Watson kernel regression, is proposed. In the proposed framework, the original Nadaraya-Watson kernel regression algorithm is enhanced by expressing the observed samples in a square kernel matrix. The WKR is trained to estimate the weight for the testing phase. The weight is estimated iteratively and ...
Kernel methods provide a principled way for general data representations. Multiple kernel learning a...
Kernel approximation is commonly used to scale kernel-based algorithms to applications contain-ing a...
Kernel methods are a broad class of algorithms that are applied in a host of scientific computing fi...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In...
In real engineering problems, regression plays an important role as a tool to approximate an unknown...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. Th...
AbstractWe obtain a unform strong approximation for the distribution of a Nadaraya-Watson kernel est...
In a regression problem the relationship between an explanatory variable X and a response variable Y...
Regression analysis is one of statistical analysis usually used to investigate the pattern of functi...
We follow a learning theory viewpoint to study a family of learning schemes for regression related t...
We present a kernel-based framework for pattern recognition, regression estimation, function approxi...
This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kern...
We present a kernel-based framework for pattern recognition, regression estimation, function approxi...
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Pr...
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Pr...
Kernel methods provide a principled way for general data representations. Multiple kernel learning a...
Kernel approximation is commonly used to scale kernel-based algorithms to applications contain-ing a...
Kernel methods are a broad class of algorithms that are applied in a host of scientific computing fi...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In...
In real engineering problems, regression plays an important role as a tool to approximate an unknown...
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. Th...
AbstractWe obtain a unform strong approximation for the distribution of a Nadaraya-Watson kernel est...
In a regression problem the relationship between an explanatory variable X and a response variable Y...
Regression analysis is one of statistical analysis usually used to investigate the pattern of functi...
We follow a learning theory viewpoint to study a family of learning schemes for regression related t...
We present a kernel-based framework for pattern recognition, regression estimation, function approxi...
This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kern...
We present a kernel-based framework for pattern recognition, regression estimation, function approxi...
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Pr...
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Pr...
Kernel methods provide a principled way for general data representations. Multiple kernel learning a...
Kernel approximation is commonly used to scale kernel-based algorithms to applications contain-ing a...
Kernel methods are a broad class of algorithms that are applied in a host of scientific computing fi...