What are the parameters for training SVM - machine-learning

Question

I'm doing software using machine learning with SVM using this library https://www.npmjs.com/package/machine_learning
according to the example of SVM:
svm.train({
C : 1.1, // default : 1.0. C in SVM.
tol : 1e-5, // default : 1e-4. Higher tolerance --> Higher precision
max_passes : 20, // default : 20. Higher max_passes --> Higher precision
alpha_tol : 1e-5, // default : 1e-5. Higher alpha_tolerance --> Higher precision
kernel : { type: "polynomial", c: 1, d: 5}
// default : {type : "gaussian", sigma : 1.0}
// {type : "gaussian", sigma : 0.5}
// {type : "linear"} // x*y
// {type : "polynomial", c : 1, d : 8} // (x*y + c)^d
// Or you can use your own kernel.
// kernel : function(vecx,vecy) { return dot(vecx,vecy);}
});
the parameter C tells the SVM optimization how much you want to avoid misclassifying each training example.
I do not understand the other parameters.

Answer 1

Just take a look at the equation of the soft-margin C-SVM:
It points out that C defines the trade-off between missclassifications and margin. This must be choosen sufficiently large depending on your data. What you'll also see here is the eps>0 parameter. This could possibly be your tolerance parameter and defines the error to the which is weighted by C parameter in the objective function.
For the kernel parameters, take a look at the dual problem for the SVM:
You'll see the term K(x_i,x_j). This is called the Kernel-Function. This function allows the SVM to learn non-linear descision boundaries. So if your data is not linearly separatable, you can use such a function to tranform your data, actually it's dot-product, into an higher dimensional feature space to separate them there. Just take a look at this guide, it will teach you the basics about the training process of an SVM and some best practices:
https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf