Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2017
Abstract
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.
Keywords
Sparse learning; Online learning; Scalable machine learning; High dimensionality
Discipline
Online and Distance Education | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Neurocomputing
Volume
260
First Page
9
Last Page
12
ISSN
0925-2312
Identifier
10.1016/j.neucom.2017.03.077
Publisher
Elsevier
Citation
WU, Yue; HOI, Steven C. H.; LIU, Chenghao; LU, Jing; SAHOO, Doyen; and YU, Nenghai.
SOL: A library for scalable online learning algorithms. (2017). Neurocomputing. 260, 9-12.
Available at: https://ink.library.smu.edu.sg/sis_research/3991
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neucom.2017.03.077
