[R] Classifying Intertwined Spirals

Liaw, Andy andy_liaw at merck.com
Fri Jan 27 21:48:58 CET 2006

You don't really expect SVM to give you good performance with no parameter
tuning at all, do you? 


m2 <- best.svm(class~., data=spiral, gamma=2^(-3:3), cost=2^(0:5))
plot(m2, spiral)


From: Joshua Gilbert
> 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/60118409@N00/91835679/
> 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.
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