Support Vector Machine Resources

• Support Vector Machines for Classification and Regression - by Steve Gunn, is a more general discussion of SVMs, focusing on use and how they fit in to the bigger picture.
• A Tutorial on Support Vector Machines for Pattern Recognition - by Christopher J.C. Burges, has lots more detailed info on the different kernels and how you decide which to use. It also has excellent figures illustrating the optimal separating hyperplane for various types of kernels.
• lots of useful info at Support Vector Machines dot org, including a lovely visual explanation of the optimum hyperplane
• R resources for SVMs are listed in the Machine Learning task view at cran
  • More on e1071 (recommended, as it has all sorts of cool stuff)
    • Here's a nice tutorial from Hannes Planatscher and Janko Dietzsch
    • e1071 listing on crantastic. You can retrieve the documentation, search the r-help archives, etc.
    • you'll almost surely need to download and install libSVM separately. I built the C++ executables, rather than the Java ones, on my mac, as follows:
      1. I downloaded the .tar.gz archive, and moved it to my user account.
      2. I opened a terminal window and unpacked the archive from the command line
      3. I ran make.
      4. My R installation found libsvm without any further configuration. Nice.
      5. There's a Makefile.win for the windowz types out there...

-- HilaryHolz - 05 Feb 2009