### Table 4: Kernel functions

"... In PAGE 11: ... Instead, we restrict attention to mappings for which the kernel K(xi; xj) = h (xi); (xj)i may be computed e ciently, without rst mapping the patterns to RD. The formulae for several common kernels are shown in Table4 . We used this property in our experiments with the SVM.... ..."

### Table 1: Kernel Functions

2005

Cited by 2

### Table 1: Kernel Functions

2005

Cited by 2

### 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 1 Kernel functions of SVMs

"... In PAGE 2: ... The kernel function K(x,xi) can be easily computed by an inner product of the non-linear mapping function. Table1 shows some rep- resentative kernel functions, including the linear, polynomial,... ..."

### Table 1. Types of kernel functions

2000

"... In PAGE 3: ...) , ( i x x K G114 G114 . Table1 shows three typical kernel functions [8]. An optimal hyperplane is constructed for separating the data in the high-dimensional feature space.... ..."

Cited by 23

### Table 1. Types of kernel functions

2000

"... In PAGE 3: ... If the two classes are non-linearly separable, the input vectors should be nonlinearly mapped to a high- dimensional feature space by an inner-product kernel function ) , ( i x x K G114 G114 . Table1 shows three typical kernel functions [10]. An optimal hyperplane is constructed for separating the data in the high-dimensional feature space.... ..."

Cited by 31

### Table 1. Types of kernel functions

2000

"... In PAGE 3: ... If the two classes are non-linearly separable, the input vectors should be nonlinearly mapped to a high- dimensional feature space by an inner-product kernel function ) , ( i x x K . Table1 shows three typical kernel functions [8]. An optimal hyperplane is constructed for separating the data in the high-dimensional feature space.... ..."

Cited by 23

### TABLE I COMMON KERNEL FUNCTIONS

2004

Cited by 1