liquidSVM: A Fast and Versatile SVM package
This provides a practical tool for researchers and practitioners in machine learning who require efficient SVM implementations, though it is incremental as it builds on existing SVM methods.
The authors tackled the need for a fast and versatile SVM package by developing liquidSVM, which achieves unprecedented speed for both small and large datasets, with support for tens of millions of samples.
liquidSVM is a package written in C++ that provides SVM-type solvers for various classification and regression tasks. Because of a fully integrated hyper-parameter selection, very carefully implemented solvers, multi-threading and GPU support, and several built-in data decomposition strategies it provides unprecedented speed for small training sizes as well as for data sets of tens of millions of samples. Besides the C++ API and a command line interface, bindings to R, MATLAB, Java, Python, and Spark are available. We present a brief description of the package and report experimental comparisons to other SVM packages.