Why Standardization = false is slow & has low accuracy?

Hi,

I am working on SVM kernel using Matlab. I have got following examples:

Quadratic SVM accuracy is 7.3%; Kernel function = quadratic, Kernel Scale Mode = Auto; Multiclass method one versus all, Box constrained level = 4 Standardized data=false

Quadratic SVM accuracy 9.1% and confusion matrix mode auto multiclass method one versus all Box constraint 10 standarddize data false PCA disable

Quadratic SVM accuracy is 9.4%; Kernel function = quadratic, Kernel Scale Mode = Auto; Multiclass method one versus one, Box constrained level = 4 Standardized data=false 2960.2s

Quadratic SVM accuracy is 19.6; Kernel function = quadratic, Kernel Scale Mode = Manual; Multiclass method one versus one, Box constrained level = 4 Standardized data=false ,2682s

In all the above examples, it generated a low accuracy and kernel took lot of time. I found that we don't do normalization and encoding in Standardization= false. So what is the reason for this bad performance.

Good accuracy is 62.8 and better with Standardization= true for quadratic and Guassian kernels. Please guide me.

Zulfi.