The C and gamma parameters influence each other, as you can see here:

C vs Gamma http://scikit-learn.org/stable/_images/plot_rbf_parameters_002.png

The performance of your solution depends on the initial fixed gamma value. If you choose a bad initial gamma value, you'll end up with a bad solution.

The easiest, but most time consuming way to find C and gamma is to test the whole grid of C x gamma values.

I often use some kind of (bayesian) optimization algorithm like this one (it's for Python, but similar should exist for R). It normally finds good C and gamma values in relatively few iterations.

PS.: the C and gamma values should be taken from a logarithmic grid of values like 10**[-5..5] - using a linear grid like [50, 60, ... ,600] won't work well.

The C and gamma parameters influence each other, as you can see here:

_{(source: scikit-learn.org)}

The performance of your solution depends on the initial fixed gamma value. If you choose a bad initial gamma value, you'll end up with a bad solution.

The easiest, but most time consuming way to find C and gamma is to test the whole grid of C x gamma values.

I often use some kind of (bayesian) optimization algorithm like this one (it's for Python, but similar should exist for R). It normally finds good C and gamma values in relatively few iterations.

PS.: the C and gamma values should be taken from a logarithmic grid of values like 10**[-5..5] - using a linear grid like [50, 60, ... ,600] won't work well.