Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding soft-margin SVMs can obtai...
Abstract. In this paper, we address the problem of determining optimal hyper-parameters for support ...
Choosing an appropriate kernel is very important and critical when classifying a new problem with Su...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...
Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised class...
The purpose of the paper is to apply a nonlinear programming algorithm for com-puting kernel and rel...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
© 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the la...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning ...
In recent years Support Vector Machines (SVM) have gained increasing popularity over other classific...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Kernel is the heart of kernel based learning. To choose an appropriate parameter for a specific kern...
Abstract. In this paper, we address the problem of determining optimal hyper-parameters for support ...
Choosing an appropriate kernel is very important and critical when classifying a new problem with Su...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...
Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised class...
The purpose of the paper is to apply a nonlinear programming algorithm for com-puting kernel and rel...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
© 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the la...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning ...
In recent years Support Vector Machines (SVM) have gained increasing popularity over other classific...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Kernel is the heart of kernel based learning. To choose an appropriate parameter for a specific kern...
Abstract. In this paper, we address the problem of determining optimal hyper-parameters for support ...
Choosing an appropriate kernel is very important and critical when classifying a new problem with Su...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...