An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines

Li, Cheng‐Hsuan; Lin, Chin‐Teng; Kuo, Bor‐Chen; Ho, Hsin-Hua

doi:10.1109/taai.2010.46

Cited by 24 publications

(38 citation statements)

References 13 publications

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“…The value of the parameter gamma () in the radial basis kernel function suggested by Fan et al [15] appeared to be more robust and powerful than other options in the simulation study (data not shown). Instead of being prespecified, this parameter could alternatively be estimated via a grid search, although this process is time consuming [26].…”

Section: Discussionmentioning

confidence: 99%

A Novel Support Vector Machine-Based Approach for Rare Variant Detection

Fang

Chiu

2013

PLoS ONE

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Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors.

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Section: Discussionmentioning

confidence: 99%

A Novel Support Vector Machine-Based Approach for Rare Variant Detection

Fang

Chiu

2013

PLoS ONE

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“…And the key to LS-SVM modelling is the selection of the kernel function and its parameters, which have a direct influence on the prediction accuracy. After contrasting kernel functions in the SVM, a radial basis function (RBF) was chosen to train the SVM in this study [27,28]. The RBF is a nonlinear function that reduces the complexity of computation during training.…”

Section: Ls-svmmentioning

confidence: 99%

A synthetic approach to weathering degree classification of stone relics case study of the Yungang Grottoes

Meng

Zhao

et al. 2018

Herit Sci

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Classification of the weathering degree of large outdoor stone relics has long been a challenge in their protection engineering. An approach to classifying the weathering levels of the Yungang Grottoes is presented in this paper. This approach is based on combined terahertz (THz) spectra and ultrasonic velocity and the use of a least squares support vector machine (LS-SVM). A regression model for prediction of the weathering level was established using the optimal values of parameters determined by the double cross-validation (D-CV) method. This SVM regression prediction model (SVMRPM) predicts the weathering levels of the grottoes with relative errors of 8.16% or less. The SVMRPM can also be used to predict the weathering levels of some areas where ultrasonic testing is extremely difficult; the THz spectra can easily be obtained using approximately 0.2 g of sample material. This method is a highly efficient and economical technique for determining the degree of weathering of large exposed stone relics.

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“…Due to this significance of SVM kernel parameter, an improved automatic selection of RBF kernel parameter is adopted here to choose the best kernel parameter sigma for the classification of fresh Wuyi rock tealeaf images. This method has been applied to SVM and GDA and has been proved to locate the optimum RBF kernel parameter sigma in a few seconds and to improve the classification performance.…”

Section: Support Vector Machine Construction With Rbf Kernel Based Onmentioning

confidence: 99%

“…Moreover, the automatic selection method is more effective and efficient than grid search. The proposed method for C is similar to the grid search in the work of the aforementioned authors . Furthermore, a finer grid is used to find the best parameter C .…”

Section: Support Vector Machine Construction With Rbf Kernel Based Onmentioning

confidence: 99%

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Automated classification of Wuyi rock tealeaves based on support vector machine

Lin

Yang

et al. 2018

Concurrency and Computation

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Summary This paper describes a new automated classification method for Wuyi rock tealeaves based on the best penalty parameter selection for the support vector machine with RBF (Radial Basis Function) kernel. A total of 3590 fresh tealeaf images of the representative Rou Gui and Shui Hsien varieties of Wuyi rock tea are collected in their natural habitat. Fourteen image features are extracted in terms of the leaf shape and texture. The automatic selection method is used to find the optimum RBF kernel parameter sigma, which is then applied to design an automatic parameter selection method to screen the best penalty parameter C for the classification of Wuyi rock tealeaves. In this study, the SVM classifier is used for the automated classification and recognition of the 14 image features. The contribution of the various features to the recognition rate of fresh tealeaves is evaluated to identify the key features for the classification and recognition of fresh Wuyi rock tealeaf images. The experimental results show that the proposed method improves the recognition rate of fresh tealeaves to 91.00%.

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An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines

Cited by 24 publications

References 13 publications

A Novel Support Vector Machine-Based Approach for Rare Variant Detection

A Novel Support Vector Machine-Based Approach for Rare Variant Detection

A synthetic approach to weathering degree classification of stone relics case study of the Yungang Grottoes

Automated classification of Wuyi rock tealeaves based on support vector machine

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