skip to main content
10.1145/2661829.2662068acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
poster

Nonlinear Classification via Linear SVMs and Multi-Task Learning

Published: 03 November 2014 Publication History

Abstract

Kernel SVM is prohibitively expensive when dealing with large nonlinear data. While ensembles of linear classifiers have been proposed to address this inefficiency, these methods are time-consuming or lack robustness. We propose an efficient classifier for nonlinear data using a new iterative learning algorithm, which partitions the data into clusters, and then trains a linear SVM for each cluster. These two steps are combined into a graphical model, with the parameters estimated efficiently using the EM algorithm. During training, clustered multi-task learning is used to capture the relatedness among the multiple linear SVMs and avoid overfitting. Experimental results on benchmark datasets show that our method outperforms state-of-the-art methods. During prediction, it also obtains comparable classification performance to kernel SVM, with much higher efficiency.

References

[1]
K. Bache and M. Lichman. UCI machine learning repository, 2013.
[2]
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
[3]
C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. ACM TIST, 2(3):27, 2011.
[4]
Z. Fu, A. Robles-Kelly, and J. Zhou. Mixing linear SVMs for nonlinear classification. IEEE TNN, 21(12):1963--1975, 2010.
[5]
Q. Gu and J. Han. Clustered support vector machines. In Proc. of AISTATS, pages 307--315, 2013.
[6]
L. Ladicky and P. Torr. Locally linear support vector machines. In Proc. of ICML, pages 985--992, 2011.
[7]
O. Wu, R. Hu, X. Mao, and W. Hu. Quality-based learning for web data classification. In Proc. of AAAI, pages 194--200, 2014.
[8]
H. Zhang, A. C. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In Proc. of CVPR, pages 2126--2136, 2006.
[9]
J. Zhou, J. Chen, and J. Ye. Clustered multi-task learning via alternating structure optimization. In Proc. of NIPS, pages 702--710, 2011.

Cited By

View all
  • (2017)Iteratively Divide-and-Conquer Learning for Nonlinear Classification and RankingACM Transactions on Intelligent Systems and Technology10.1145/31228029:2(1-26)Online publication date: 23-Oct-2017
  • (2017)Predicting Big-Five Personality for Micro-blog Based on Robust Multi-task LearningData Science10.1007/978-981-10-6385-5_41(486-499)Online publication date: 16-Sep-2017

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
November 2014
2152 pages
ISBN:9781450325981
DOI:10.1145/2661829
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. linear svms
  2. multi-task learning
  3. nonlinear classification

Qualifiers

  • Poster

Funding Sources

Conference

CIKM '14
Sponsor:

Acceptance Rates

CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2017)Iteratively Divide-and-Conquer Learning for Nonlinear Classification and RankingACM Transactions on Intelligent Systems and Technology10.1145/31228029:2(1-26)Online publication date: 23-Oct-2017
  • (2017)Predicting Big-Five Personality for Micro-blog Based on Robust Multi-task LearningData Science10.1007/978-981-10-6385-5_41(486-499)Online publication date: 16-Sep-2017

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media