# liquidSVM: A Fast and Versatile SVM package

arXiv: https://arxiv.org/abs/1702.06899 · HTML: https://mlsanity.com/paper/1702.06899/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features](https://mlsanity.com/paper/2104.00629/) (2021)
- [Learning Prediction Intervals for Regression: Generalization and Calibration](https://mlsanity.com/paper/2102.13625/) (2021)
- [Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression](https://mlsanity.com/paper/2201.06314/) (2022)
- [An Exact Solver for the Weston-Watkins SVM Subproblem](https://mlsanity.com/paper/2102.05640/) (2021)
- [Snacks: a fast large-scale kernel SVM solver](https://mlsanity.com/paper/2304.07983/) (2023)
- [Bags of Projected Nearest Neighbours: Competitors to Random Forests?](https://mlsanity.com/paper/2503.09651/) (2025)
- [Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach](https://mlsanity.com/paper/2606.00265/) (2026)

## Abstract (arXiv)

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.