SOL: A Library for Scalable Online Learning Algorithms

TL;DR

SOL is an open-source C++ library offering scalable, efficient online learning algorithms suitable for high-dimensional data, supporting large-scale classification tasks with extensive tools and research capabilities.

Contribution

Introduces a comprehensive, scalable online learning library with diverse algorithms, tools, and experimental platform for research and practical applications.

Findings

Demonstrates high efficiency and scalability in large-scale, high-dimensional data tasks.

Provides versatile tools and wrappers for easy adoption and experimentation.

Supports both binary and multi-class classification problems.

Abstract

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.