SOL: A Library for Scalable Online Learning Algorithms
This is an incremental contribution providing a practical toolbox for researchers and developers working with large-scale online learning problems.
The authors developed SOL, an open-source library for scalable online learning algorithms that is particularly suitable for high-dimensional data, providing efficient and scalable tools for large-scale binary and multi-class classification tasks.
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.