2010
DOI: 10.1007/978-3-642-15766-0_93
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A Comparative Study and Choice of an Appropriate Kernel for Support Vector Machines

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Cited by 14 publications
(10 citation statements)
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“…Table 4 shows the specificity and the sensitivity for the kernel functions already described. The complexity of the various kernel functions can be studied by considering the relative number of vectors required and also the relative execution time [26,27]. The kernel with the most significant values for the parameters is considered.…”
Section: Resultsmentioning
confidence: 99%
“…Table 4 shows the specificity and the sensitivity for the kernel functions already described. The complexity of the various kernel functions can be studied by considering the relative number of vectors required and also the relative execution time [26,27]. The kernel with the most significant values for the parameters is considered.…”
Section: Resultsmentioning
confidence: 99%
“…One of the hyperplanes that maximizes the margin is an optimal separating hyperplane. Binary classification and its kernel selection are explained in [12,13]. [15] Many real world problems like circuit diagnosis, natural language processing come under the category of multiclass support vector machines.…”
Section: Support Vector Machinesmentioning
confidence: 99%
“…Data that are close to each other in local kernels influence on the kernel points and data that are far away from each other in global kernels influence on the kernel points. Commonly used kernels like Linear, Polynomial, RBF, Sigmoid are discussed in [12,13] and used in this paper. Also, there are some more kernels which are represented in Table 1.Kernel functions and its transformation largely depends on the domain .So, selecting a suitable kernel function with its parameter is a major research area in multiclass support vector machine.…”
Section: Kernels In Multiclass Svmmentioning
confidence: 99%
“…The SVM employs the risk minimisation theory to establish the best separation hyperplane in multi-dimensional space to classify a bipartite outcome [11]. Initially, the SVM was designed for binary classification [12]; however, of late, the SVM is applicable for both classification and The performance of the SVM has been compared with other ML algorithms, such as Bayesian logistic regression, and decision tree [13], [14], random forest [15], [16], neural network [17], [18] and k-nearest neighbours [19], [20]. Notwithstanding variations in the experimental outcomes, the SVM is equated to more traditional models in many of these studies.…”
Section: Introductionmentioning
confidence: 99%