SOL: A library for scalable online learning algorithms

• Main Authors: WU, Yue, HOI, Steven C. H., LIU, Chenghao, LU, Jing, SAHOO, Doyen, YU, Nenghai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Sparse learning; Online learning; Scalable machine learning; High dimensionality; Online and Distance Education; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3991; https://ink.library.smu.edu.sg/context/sis_research/article/4993/viewcontent/SOL__A_library_for_scalable_online_learning_algorithms.pdf
• Institution: Singapore Management University

SOL is an open-source library for scalable online learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale classification tasks with high efficiency, scalability, portability, and extensibility. We provide easy-to-use command-line tools, python wrappers and library calls for users and developers, and comprehensive documents for both beginners and advanced users. SOL is not only a 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 learning with high-dimensional data.