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Knowledge based proximal support vector machines

Author

Listed:
  • Khemchandani, Reshma
  • Jayadeva
  • Chandra, Suresh

Abstract

We propose a proximal version of the knowledge based support vector machine formulation, termed as knowledge based proximal support vector machines (KBPSVMs) in the sequel, for binary data classification. The KBPSVM classifier incorporates prior knowledge in the form of multiple polyhedral sets, and determines two parallel planes that are kept as distant from each other as possible. The proposed algorithm is simple and fast as no quadratic programming solver needs to be employed. Effectively, only the solution of a structured system of linear equations is needed.

Suggested Citation

  • Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2009. "Knowledge based proximal support vector machines," European Journal of Operational Research, Elsevier, vol. 195(3), pages 914-923, June.
    • Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:914-923
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    Citations

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    Cited by:

    1. Sunoj, S. & Polson, Benjamin & Vaish, Isha & Marcaida, Manuel & Longchamps, Louis & van Aardt, Jan & Ketterings, Quirine M., 2024. "Corn grain and silage yield class prediction for zone delineation using high-resolution satellite imagery," Agricultural Systems, Elsevier, vol. 218(C).
    2. Khemchandani, Reshma & Jayadeva & Chandra, Suresh, 2010. "Learning the optimal kernel for Fisher discriminant analysis via second order cone programming," European Journal of Operational Research, Elsevier, vol. 203(3), pages 692-697, June.
    3. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
    4. Kaiquan Xu & Stephen Shaoyi Liao & Raymond Y. K. Lau & J. Leon Zhao, 2014. "Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 461-483, August.
    5. Aytug, Haldun & Sayın, Serpil, 2012. "Exploring the trade-off between generalization and empirical errors in a one-norm SVM," European Journal of Operational Research, Elsevier, vol. 218(3), pages 667-675.
    6. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    7. Cassioli, A. & Chiavaioli, A. & Manes, C. & Sciandrone, M., 2013. "An incremental least squares algorithm for large scale linear classification," European Journal of Operational Research, Elsevier, vol. 224(3), pages 560-565.

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