### Table 1. Common kernels

2002

"... In PAGE 2: ... Optimizing the SVM hyper-parameters is a model selection problem that needs adapting multiple parameter values at the same time. The parameters to tune are those that embed any kernel function as the parameter in an RBF kernel or the couple ( ; ) in case of KMOD kernel (see Table1 ). In addition, another parameter the optimization may consider is the trade-off parameter C which may have a strong effect on the SVM behavior for hard classification tasks.... ..."

Cited by 4

### TABLE I COMMON KERNEL FUNCTIONS

2004

Cited by 1

### Table 2: The commonly accepted kernel functions Typical Kernel function Explanation

"... In PAGE 3: ... A large C assigns a high penalty to the classi cation errors[12]. The most commonly accepted employed kernel functions are shown in Table2 . For a given training set, only the kernel function and the parameter C are needed to be selected to specify one SVM.... ..."

### Table 2.1: Common kernel functions

2006

### Table 1. Commonly used kernel functions.

### Table 1. Commonly used SVM kernel functions and their parameters

2004

"... In PAGE 4: ... The kernel functions can sometimes be categorized as local kernels (Gaussian, KMOD) and global ker- nels (linear, polynomial, sigmoidal) where local kernels attempt to measure the proximity of data samples and are based on a distance function rather than dot-product based global kernels. Table1 lists the kernel expressions and cor- responding parameters. Note that lt; x, y gt; represents dotproduct, where x and y denote two arbitrary feature vectors.... In PAGE 4: ... In addition, each of the kernel functions have varying number of free parameters which can be selected by the teacher. As can be seen from Table1 , the performance of an SVM using linear, Gaussian, or polynomial kernels is dependent upon one, two, and four parameters respectively. All the kernels share one common parameter C, the constant of constraint violation which observes the occurring of a data sample on the wrong side of the decision boundary.... ..."

Cited by 1

### Table 1 shows some of the common trends for those kernels.

2003

Cited by 17