- SVMTorch (support vector machines for large-scale regression problems) enforced within the Torch machine learning library.
- mySVM - based on the optimization algorithm of SVM-Light.
- SVMLight before and located it to be very stable and quick. I had decent expertise using it and would suggest it.
However, I think there is probably less documentation on SVMLight than libSVM; just the papers by Thorsten Joachims and the comments in the source code. I didn't find the source too hard to follow in general, but you need to read the papers beforehand to understand the background. It's also written in pure C, not C++ if that matters to you.
As for 'new players', the new research is mostly into making the SVM optimization algorithms more efficient. For example, using stochastic gradient descent as in svmsgd and pegasus. I haven't looked at the implementations of these algorithms, but it's research code so I wouldn't expect that they are particularly easy to follow, if that's your primary concern.
SHARK may be a standard C++ library for the design and optimization of adaptative systems. It provides strategies for linear and nonlinear optimization, in particular, evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques. SHARK is a tool case to support universe applications additionally as analysis in several domains of procedure intelligence and machine learning. The sources are compatible with the subsequent platforms: Windows, Solaris, macOS X, and Linux.
For more details, SVM will be used for a better thing to learn.
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