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This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples ...
Sep 30, 2008 · This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have ...
This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples ...
This paper presents a novel algorithm to optimize the parameters of a Gaussian kernel for pattern classification tasks. The algorithm aims to maximize a ...
Gaussian kernel is a popular kernel function often used in various statistical pattern recognition researches and their applications to measure the ...
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Abstract—This paper establishes a fitting method for a kernel logistic regression model that uses generalized Gaussian kernel and its parameter optimization ...
This paper presents a novel fast method to optimize the Gaussian kernel function for two-class pattern classification tasks, where it is desirable for the ...
This paper presents a novel algorithm to optimize the Gaussian kernel for pattern classification tasks, where it is desirable to have well-separated samples ...
The main advantage of the kernel methods is the possibility of using linear models in a nonlinear subspace by an implicit transformation of patterns to a ...
This paper presents a novel fast method to optimize the Gaussian kernel function for two-class pattern classification tasks, where it is desirable for the ...