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LIBOL: a library for online learning algorithms

Editors: Kevin Murphy, Bernhard Schölkopf Authors:

Steven C. H. Hoi,

Peilin ZhaoAuthors Info & Claims

The Journal of Machine Learning Research, Volume 15, Issue 1

Pages 495 - 499

Published: 01 January 2014 Publication History

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Abstract

LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large-scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research.

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Cited By

Xiao ZZhang L(2024)Regret analysis of an online majorized semi-proximal ADMM for online composite optimizationJournal of Global Optimization10.1007/s10898-024-01365-589:3(687-722)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10898-024-01365-5
You DXiao JWang YYan HWu DChen ZShen LWu X(2023)Online Learning From Incomplete and Imbalanced Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325047235:10(10650-10665)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250472
Odysseos LHerodotou H(2023)On combining system and machine learning performance tuning for distributed data stream applicationsDistributed and Parallel Databases10.1007/s10619-023-07434-041:3(411-438)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s10619-023-07434-0
Show More Cited By

LIBOL: a library for online learning algorithms
1. Computing methodologies
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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Information

Published In

cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 15, Issue 1

January 2014

4085 pages

ISSN:1532-4435

EISSN:1533-7928

Editors:
Kevin Murphy
Google
,
Bernhard Schölkopf

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JMLR.org

Publication History

Published: 01 January 2014

Revised: 01 December 2013

Published in JMLR Volume 15, Issue 1

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Citations

Cited By

Xiao ZZhang L(2024)Regret analysis of an online majorized semi-proximal ADMM for online composite optimizationJournal of Global Optimization10.1007/s10898-024-01365-589:3(687-722)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s10898-024-01365-5
You DXiao JWang YYan HWu DChen ZShen LWu X(2023)Online Learning From Incomplete and Imbalanced Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325047235:10(10650-10665)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3250472
Odysseos LHerodotou H(2023)On combining system and machine learning performance tuning for distributed data stream applicationsDistributed and Parallel Databases10.1007/s10619-023-07434-041:3(411-438)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s10619-023-07434-0
Zheng TWen Z(2022)Online Convolutional Neural Network for Image Streams ClassificationProceedings of the 5th International Conference on Big Data Technologies10.1145/3565291.3565332(255-259)Online publication date: 23-Sep-2022
https://dl.acm.org/doi/10.1145/3565291.3565332
Dong JLiu D(2022)Sensor Data Prediction for Fixed-wing Drone Based on Online Sequential Learning2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC48687.2022.9806574(1-5)Online publication date: 16-May-2022
https://dl.acm.org/doi/10.1109/I2MTC48687.2022.9806574
Dai SMeng F(2022)Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learningApplied Intelligence10.1007/s10489-022-04065-353:9(11045-11072)Online publication date: 30-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04065-3
Chen ZSheng VEdwards AZhang K(2022)An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detectionKnowledge and Information Systems10.1007/s10115-022-01745-x65:1(59-87)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1007/s10115-022-01745-x
Schreckenberger CBartelt CStuckenschmidt H(2022)Dynamic Forest for Learning from Data Streams with Varying Feature SpacesCooperative Information Systems10.1007/978-3-031-17834-4_6(95-111)Online publication date: 4-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-17834-4_6
Luo SXu HBi YWang XZhou YCadar CZhang X(2021)Boosting symbolic execution via constraint solving time prediction (experience paper)Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3460319.3464813(336-347)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3460319.3464813
Aslam ACurry E(2021)Investigating response time and accuracy in online classifier learning for multimedia publish-subscribe systemsMultimedia Tools and Applications10.1007/s11042-020-10277-x80:9(13021-13057)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s11042-020-10277-x
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