I am currently playing around with it. Decent support vector machine implementation. However, I have some problems with it. It is by far not as hugh as weka http://www.cs.waikato.ac.nz/ml/weka/
It is in python (big plus) and seems to be easily hacked for online classification.
I like https://mlpy.fbk.eu/ a bit better, as it has also a decent integrated Nearest Neighbor (ok, ok this one is not hard to implement on your own) and FDA + DWT (awsome).
Google, being a good friend of mine, didn't mind me asking. His answer: Fisher Discriminant Analysis (FDA), Discrete Wavelet Transform (DWT). I'll introduce you: http://google.com
PySVM might have been a better name, since it looks like it only does SVM and kernel methods. I haven't used this package, but I'll recommend libsvm and liblinear because they're fast and have wrappers for just about every language you want, including Java, Ruby, Matlab, and Python http://www.csie.ntu.edu.tw/~cjlin/libsvm/#python
"By default the libsvm solver is used in training. To use the PyML SMO optimizer either set the optimizer attribute to ’mysmo’ or instantiate an svm instance as svm.SVM(optimizer = ’mysmo’). Note that for a non-vector dataset, the default libsvm optimizer cannot be used and the PyML native SMO implementation is automatically used instead (it is slower than libsvm so is not the default)"
One serious problem of HN is that the amount of good stuff on here greatly exceeds my capacity for absorbing it all... Roughly one week goes by and I have at least another months worth of reading added to my list.
Well, it is academic software. I work at ailab.si and a lot of my work concerns Orange, especially its bioinformatics addon. Its development is slow because we also need to do some other things, like research. New features are usually added when we need them.
It is actively maintained/developed but by only few developers, so its progress can seem slow at times.
