I'm using an SVM as I've seen a paper that reported extremely good results. I'm not having such luck. I'm also interested in ideas for other approaches to the problem that can also be applied to general problems (no assuming that we're looking for spirals).
Here is my code: library(mlbench) library(e1071) raw <- mlbench.spirals(194, 2) spiral <- data.frame(class=as.factor(raw$classes), xx=raw$x[,1], y=raw$x[,2]) m <- svm(class~., data=spiral) plot(m, spiral) You'll note that I have two spirals with 97 points each and I'm using a kernel with a radial basis: exp(-gamma*|u-v|^2). You should be able to see a PNG of the resulting plot here: http://www.flickr.com/photos/[EMAIL PROTECTED]/91835679/ The problem is that that's not good enough. I want a better fit. I think I can get one, I just don't know how. There's a paper on Proximal SVMs that claims a better result. To the best of my knowledge, PSVMs should not outperform SVMs, they are merely faster to compute. You can find the paper (with the picture of their SVM) on citeseer: http://citeseer.ifi.unizh.ch/cachedpage/515368/5 @misc{ fung-proximal, author = "G. Fung and O. Mangasarian", title = "Proximal support vector machine classifiers", text = "G. Fung and O. Mangasarian. Proximal support vector machine classifiers. In F. P. D. Lee and R. Srikant, editors, KDD", url = "citeseer.ifi.unizh.ch/515368.html" } I don't have much of a background in SVMs, I'm learning as I go, so please don't hold back 'simple-minded' suggestions. I'm also asking the authors, but I'm not expecting a reply from them. There was a paper by Lang and Whitbrock in 1988 (Learning to Tell Two Spirals Apart) that solved the problem with a neural network, but they used a very specialized network architecture. I would say that discovering such an architecture and then optimizing it would be very time-intensive. Thank you for any response. Josh. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
