Skip to main content
SpringerLink
Log in

How to Gauge Accuracy of Processing Big Data: Teaching Machine Learning Techniques to Gauge Their Own Accuracy

  • Conference paper
  • First Online:
Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

Included in the following conference series:

  • 2124 Accesses

Abstract

When the amount of data is reasonably small, we can usually fit this data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model’s accuracy. For big data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how to modify the existing machine learning methodology so that it will not only estimate the parameters, but also estimate the accuracy of the resulting model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  2. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  3. Mayer-Schönberger, V., Cukier, K.: Big Data: The Essential Guide to Work, Life, and Learning in the Age of Insight. John Murray Publishers, London (2017)

    Google Scholar 

  4. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, Boca Raton, Florida (2011)

    MATH  Google Scholar 

  5. Turchetti, C.: Stochastic Models of Neural Networks. IOS Press, Amsterdam (2004)

    MATH  Google Scholar 

  6. Werbos, P.: A brain-like design to learn optimal decision strategies in complex environments. In: Karny, M., Warwick, K., Kurkova, V. (eds.) Dealing with Complexity: A Neural Networks Approach. Springer, London (1998)

    Google Scholar 

  7. Werbos, P.: Intelligence in the brain: a theory of how it works and how to build it. Neural Netw. 22(3), 200–212 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Chiang Mai University, Thailand. This work was also supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, and by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation.

One of the authors (VK) is thankful to Paul Werbos for inspiring talks and discussions.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, 500 W. University, El Paso, TX, 79968, USA

    Vladik Kreinovich & Olga Kosheleva

  2. Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

    Thongchai Dumrongpokaphan

  3. Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88003, USA

    Hung T. Nguyen

  4. Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Hung T. Nguyen

Authors
  1. Vladik Kreinovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thongchai Dumrongpokaphan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hung T. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olga Kosheleva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kreinovich, V., Dumrongpokaphan, T., Nguyen, H.T., Kosheleva, O. (2018). How to Gauge Accuracy of Processing Big Data: Teaching Machine Learning Techniques to Gauge Their Own Accuracy. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70942-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation