Happy new year everyone! I'm recovering from surgery nicely, and hoping to start the new year with a more upward trend on matters related to my health. This has really slowed down a lot of my plans for JSAT, but I hope it still be useful to everyone. I'm going to prepare a release of the current changes, which may be of interest to some. I added two new SVM classes, one called SVMnoBias, which is useful for most kernels (it has to be normalized, which the RBF kernel and similar friends are) people are interested in. This new SVM class is faster than the PlattSMO one, and used to implement the Divide and Conquer SVM solver, which is supposed to be very fast despite being an approximation when using early stopping (which is the default). I haven't tested this on any super large datasets yet, but initial results looked promising. The Kernel K-Means also got improvements in runtime.
Some of the stuff I'm planning to work on in the near future include a follow up to the AMM, called the Convex Polytope Machine. I'm also hoping to round out some more of the kernel methods, as a few recent papers have proposed some interesting looking approximate feature space algorithms for the RBF kernel that would be a good improvement of the Random Fourier Features currently implemented. I've also received a few requests about Neural Networks in JSAT. While I think there are a lot of much better Neural Network tools out there (especially when it comes to using GPUs), I'm going to go back and add Batch Normalization as a layer option. I've thought about adding convolutions as well, but I think there are too many great tools for that use case. Anything I implement is going to be considerably slower, especially without a GPU, so I'd rather people use other, better, tools instead.
Longer term I'm thinking of putting some of the improvements discussed in the XGBoost paper into JSAT.
