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Making large-scale SVM learning practical

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  • Joachims, Thorsten

Abstract

Training a support vector machine SVM leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples on the shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical. The results give guidelines for the application of SVMs to large domains.

Suggested Citation

  • Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199828
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    File URL: https://www.econstor.eu/bitstream/10419/77178/2/1998-28.pdf
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    1. Luminita STATE & Catalina COCIANU & Cristian USCATU & Marinela MIRCEA, 2013. "Extensions of the SVM Method to the Non-Linearly Separable Data," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 173-182.
    2. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Daniel Horn & Aydın Demircioğlu & Bernd Bischl & Tobias Glasmachers & Claus Weihs, 2018. "A comparative study on large scale kernelized support vector machines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 867-883, December.
    4. Luca Zanni, 2006. "An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines," Computational Management Science, Springer, vol. 3(2), pages 131-145, April.
    5. C. J. Lin & S. Lucidi & L. Palagi & A. Risi & M. Sciandrone, 2009. "Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 107-126, April.
    6. Hoi-Ming Chi & Okan K. Ersoy & Herbert Moskowitz & Kemal Altinkemer, 2007. "Toward Automated Intelligent Manufacturing Systems (AIMS)," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 302-312, May.
    7. Weiwei Ding & Yuhong Zhang & Liya Huang, 2022. "Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks," IJERPH, MDPI, vol. 19(6), pages 1-14, March.
    8. Farah Mohammad & Saad Al Ahmadi, 2023. "Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    9. Peng Han & Xinyue Yang & Yifei Zhao & Xiangmin Guan & Shengjie Wang, 2022. "Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network," Sustainability, MDPI, vol. 14(9), pages 1-13, May.
    10. Heguang Sun & Lin Zhou & Meiyan Shu & Jie Zhang & Ziheng Feng & Haikuan Feng & Xiaoyu Song & Jibo Yue & Wei Guo, 2024. "Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation," Agriculture, MDPI, vol. 14(3), pages 1-18, March.
    11. Wang, Yongqiang & Huang, Donghua & Sun, Kexin & Shen, Hongzheng & Xing, Xuguang & Liu, Xiao & Ma, Xiaoyi, 2023. "Multiobjective optimization of regional irrigation and nitrogen schedules by using the CERES-Maize model with crop parameters determined from the remotely sensed leaf area index," Agricultural Water Management, Elsevier, vol. 286(C).
    12. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    13. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    14. Andrea Manno & Laura Palagi & Simone Sagratella, 2014. "A Class of Convergent Parallel Algorithms for SVMs Training," DIAG Technical Reports 2014-17, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    15. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    16. Sachin Kumar & Aditya Sharma & B Kartheek Reddy & Shreyas Sachan & Vaibhav Jain & Jagvinder Singh, 2022. "An intelligent model based on integrated inverse document frequency and multinomial Naive Bayes for current affairs news categorisation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1341-1355, June.
    17. Luminita STATE & Catalina COCIANU & Doina FUSARU, 2010. "A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(3), pages 128-139.
    18. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    19. Tianrui Yin & Wei Chen & Bo Liu & Changzhen Li & Luyao Du, 2023. "Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions," Mathematics, MDPI, vol. 12(1), pages 1-19, December.

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