I released a new version of JKernelMachines with the following features:
  • new algorithms: SDCA (Shalev-Shwartz 2013), SAG (Le Roux 2012)
  • new custom matrix kernel to handle train and test separately
  • add fvec file format
  • add experimental package for linear algebra and corresponding processing (i.e. PCA, KPCA), use at your own risk!
  • add example app to perform VOC style classification
  • Lots of bug fixes

The linear algebra package is at the moment very rough. I find it somehow useful to perform some king of pre-processing (like a PCA for example). At the moment, my matrix code is a bit slow. If ever I find the time to make solid matrix operations, I will add some nice features like low rank approximations of kernels (Nyström).

Nevertheless, I suggest to always pick the latest git version instead of these releases. The API is very stable now and should not change significantly, which means that all the code you write now is to be supported in the next few years. Thus, picking the latest git always assures you to have the bug-fixes and so on (I don't release versions only for bug-fixes).

One more thing: JKernelMachines has been published in JMLR last month. I encourage you to read the paper and to cite it if you ever use to code for your publications.