Skip to main content

Extension III: Variational Margin Settings within Local Data in Support Vector Regression

  • Chapter
Machine Learning

Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

  • 6164 Accesses

Abstract

In Chapter 6, we propose a Local Support Vector Regression Model to include the local information of data. In this chapter, we consider another extension of the Support Vector Regression (SVR) which also includes the local information of data for a specific application, i. e. financial engineering. Both these models are motivated from the local viewpoint of data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Gustavo M, deAthayde (2001) Building a Mean-downside Risk Portfolio Frontier. In: Sortino F.A, Satchell S.E, editors, Managing Downside Risk in Financial Markets: Theory, Practice and Implementation. Oxford, Boston: utterworth-Heinemann 194–211

    Google Scholar 

  2. Baird IS, Howard T (1990) What Is Risk Anyway? Using and Measuring Risk in Strategic Management. In Bettis Richard A and Thomas Howard, editors, Risk, Strategy and Management. Greenwich, Conn: JAI Press 21–51

    Google Scholar 

  3. Bollerslev T (1986) Generalized Autoregressive Conditional Heteroskedasticity. Econometrics 31:307–327

    Article  MATH  MathSciNet  Google Scholar 

  4. Cao LJ, Chua KS, Guan LK (2003) c-Ascending Support Vector Machines for Financial Time Series Forecasting. In International Conference on Computational Intelligence for Financial Engineering (CIFEr2003) 329–335

    Google Scholar 

  5. Chang CC, Lin CJ (2001) LIBSVM: A Library for Support Vector Machines

    Google Scholar 

  6. Cristianini N, Shawe-Taylor J (2000) An Introduction to Support Vector Machines(and Other Kernel-based Learning Methods). Cambridge, U.K.; New York: Cambridge University Press

    Google Scholar 

  7. Hastie T, Rosset S, Tibshirani R, Zhu J (2004) The entire regularization path for the support vector machine. Journal of Machine Learning Research 5:1391–1415

    MathSciNet  Google Scholar 

  8. Markowitz H (1952) Portfolio Selection. Journal of Finance 7:77–91

    Article  Google Scholar 

  9. Mukherjee S, Osuna E, Girosi F (1997) Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines. In Principe J, Giles L, Morgan N, Wilson E, editors, IEEE Workshop on Neural Networks for Signal Processing VII. IEEE Press 511–519

    Google Scholar 

  10. Müller KR, Smola A, Rätsch G, Schölkopf B, Kohlmorgen J, Vapnik V (1997) Predicting Time Series with Support Vector Machines. In Gerstner W, Germond A, Hasler M, and Nicoud JD, editors, ICANN. New York, NY: Springer 999–1004

    Chapter  Google Scholar 

  11. Nabney IT (2002) Netlab: Algorithms for Pattern Recognition. New York, NY: Springer

    MATH  Google Scholar 

  12. Schölkopf B, Chen PH, Lin CJ (2003) A Tutorial on ν-Support Vector Machines. Technical Report, National Taiwan University

    Google Scholar 

  13. Schölkopf B, Bartlett P, Smola A, Williamson R (1998) Support Vector Regression with Automatic Accuracy Control. In Niklasson L, Bodén M, and Ziemke T, editors, Proceedings of ICANN’98 Perspectives in Neural Computing. Berlin 111–116

    Google Scholar 

  14. Schölkopf B, Bartlett P, Smola A, Williamson R (1999) Shrinking the Tube: A New Support Vector Regression Algorithm. In Kearns MS, Solla SA, Cohn DA, editors, Advances in Neural Information Processing Systems. Cambridge, MA: The MIT Press 11: 330–336

    Google Scholar 

  15. Schölkopf B, Smola AJ, Williamson R, Bartlett P (1998) New Support Vector Algorithms. Technical Report NC2-TR-1998-031, GMD and Australian National University

    Google Scholar 

  16. Smola A, Schölkopf B (1998) A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2

    Google Scholar 

  17. Smola AJ, Murata N, Schölkopf B, Müller KR (1998) Asymptotically Optimal Choice of ε-Loss for Support Vector Machines. In Proc. of Seventeenth Intl. Conf. on Artificial Neural Networks

    Google Scholar 

  18. Trafalis TB, Ince H (2000) Support Vector Machine for Regression and Applications to Financial Forecasting. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN2000). IEEE 6: 348–353

    Google Scholar 

  19. Vapnik VN (1999) The Nature of Statistical Learning Theory. New York, NY: Springer, 2nd edition

    Google Scholar 

  20. Vapnik VN, Golowich S, Smola AJ (1997) Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. In Mozer M, Jordan M, Petshe T, editors, Advances in Neural Information Processing Systems. Cambridge, MA: The MIT Press 9: 281–287

    Google Scholar 

  21. Wang G, Yeung DY, Lochovsky FH (2006) Two-dimensional solution path for support vector regression. In The 23rd International Conference on Machine Learning. Pittsburge, PA: 1993–1000

    Google Scholar 

  22. Yang H, Chan L, King I (2002) Support Vector Machine Regression for Volatile Stock Market Prediction. In Yin Hujun, Allinson Nigel, Freeman Richard, Keane John, and Hubbard Simon, editors, Intelligent Data Engineering and Automated Learning — IDEAL 2002. New York, NY: Springer 2412 of LNCS: 391–396

    Chapter  Google Scholar 

  23. Yang H, King I, Chan L (2002) Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction Using Support Vector Regression. In International Conference on Neural Information Processing — ICONIP 2002, 1968

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Berlin Heidelberg

About this chapter

Cite this chapter

(2008). Extension III: Variational Margin Settings within Local Data in Support Vector Regression. In: Machine Learning. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79452-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-79452-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79451-6

  • Online ISBN: 978-3-540-79452-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics