Re: Q9, SVM vs PLA
@Catherine:
I also attempted to use ipop in the R kernlab package. I was having issues with the upper u constraint bounding the alphas. Depending on the u value I used, I'd get more volatility on the differences in the b values (I mean the bias b term in the weights). As many in other threads have pointed out, you never get any alphas equal to zero, just really low values on the order of <10^(5). No matter if I calculated the weight vector summing up over all alphas or just wiping out those alphas close to zero, my bias terms were not equal when I solved for the support vectors. What really rang the alarm bells though was that the Ein error rate for the proposed solution obtained through the quadratic programming routine was never zero. Furthermore, sometimes the ipop returned with errors.
So I opted for using the ksvm function in the same package to obtain the support vector solutions and thereafter usign predict to calculate the out of sample error rate (with a large test data set). The ksvm function always returned an insample error of zero but, although I got question 8 correct using it, I failed to get questions 9 and 10 correctly.
Could you indicate how you got the ipop function to work? what parameters did you feed it? Did u use "vanilladot" as the kernel function for the H matrix?
