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Support vector machines

Author

Listed:
  • Nick Guenther

    (University of Waterloo)

  • Matthias Schonlau

    (University of Waterloo)

Abstract

Support vector machines are statistical- and machine-learning techniques with the primary goal of prediction. They can be applied to continuous, binary, and categorical outcomes analogous to Gaussian, logistic, and multinomial regression. We introduce a new command for this purpose, svmachines. This package is a thin wrapper for the widely deployed libsvm (Chang and Lin, 2011, ACM Transactions on Intelligent Systems and Technology 2(3): Article 27). We illustrate svmachines with two examples. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LP, vol. 16(4), pages 917-937, December.
    • Handle: RePEc:tsj:stataj:v:16:y:2016:i:4:p:917-937
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    Citations

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    Cited by:

    1. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    2. Salman Khalid & Hyunho Hwang & Heung Soo Kim, 2021. "Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant," Mathematics, MDPI, vol. 9(21), pages 1-27, November.
    3. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    4. Roberson Andrea, 2021. "Applying Machine Learning for Automatic Product Categorization," Journal of Official Statistics, Sciendo, vol. 37(2), pages 395-410, June.
    5. Arthur C. Santos & Wesley A. Souza & Gustavo V. Barbara & Marcelo F. Castoldi & Alessandro Goedtel, 2023. "Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    6. Gründler, Klaus & Krieger, Tommy, 2021. "Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019," European Journal of Political Economy, Elsevier, vol. 70(C).
    7. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    8. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    9. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2023. "pystacked: Stacking generalization and machine learning in Stata," Stata Journal, StataCorp LP, vol. 23(4), pages 909-931, December.
    10. Yi Yang & Yuting Bai & Xiaoyi Wang & Li Wang & Xuebo Jin & Qian Sun, 2020. "Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    11. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    12. Na Tang & Maoxiang Yuan & Zhijun Chen & Jian Ma & Rui Sun & Yide Yang & Quanyuan He & Xiaowei Guo & Shixiong Hu & Junhua Zhou, 2023. "Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    13. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.

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