APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
The effectiveness of an SVM depends upon
|
|
Selection of Kernel
|
|
|
Kernel Parameters
|
|
|
Soft Margin Parameter C
|
|
|
All of the above
|
Explanation:
Detailed explanation-1: -The effectiveness of an SVM depends upon the kernel function, parameters of the kernel and the soft margin parameter C.
Detailed explanation-2: -The referred SVM function has the following parameters to be defined: the error tolerance ( or C), the pyramid depths (P), the radial basis function parameter (), and the threshold. The most effective combination of input parameters to RBF was C = 100; P = 2, = 0.1, threshold = 0.05.
Detailed explanation-3: -The effectiveness of SVM depends on the selection of kernel, kernel’s parameters and soft margin parameter C. . Each pair of parameters is checked using cross validation, and the parameters with best cross validation accuracy are picked.
There is 1 question to complete.