# SOL: A Library for Scalable Online Learning Algorithms

arXiv: https://arxiv.org/abs/1610.09083 · HTML: https://mlsanity.com/paper/1610.09083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Cybersecurity Information Exchange with Privacy (CYBEX-P) and TAHOE -- A Cyberthreat Language](https://mlsanity.com/paper/2106.01632/) (2021)

## Abstract (arXiv)

SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, portability, and extensibility. SOL was implemented in C++, and provided with a collection of easy-to-use command-line tools, python wrappers and library calls for users and developers, as well as comprehensive documents for both beginners and advanced users. SOL is not only a practical machine learning toolbox, but also a comprehensive experimental platform for online learning research. Experiments demonstrate that SOL is highly efficient and scalable for large-scale machine learning with high-dimensional data.