SOL: A Library for Scalable Online Learning Algorithms

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Authors Jing Lu, Doyen Sahoo, Yue Wu, Nenghai Yu, Steven C. H. Hoi, Chenghao Liu
Journal/Conference Name Neurocomputing
Paper Category
Paper 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.
Date of publication 2016
Code Programming Language Terra
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