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Semi-supervised Smoothing for Large Data Problems

  • Mark Vere Culp
  • Kenneth Joseph Ryan
  • George Michailidis
Chapter
Part of the Springer Handbooks of Computational Statistics book series (SHCS)

Abstract

This book chapter is a description of some recent developments in non-parametric semi-supervised regression and is intended for someone with a background in statistics, computer science, or data sciences who is familiar with local kernel smoothing (Hastie et al., The elements of statistical learning (data mining, inference and prediction), chapter 6. Springer, Berlin, 2009). In many applications, response data often require substantially more effort to obtain than feature data. Semi-supervised learning approaches are designed to explicitly train a classifier or regressor using all the available responses and the full feature data. This presentation is focused on local kernel regression methods in semi-supervised learning and provides a good starting point for understanding semi-supervised methods in general.

Keywords

Computational statistics Machine learning Non-parametric regression 

Notes

Acknowledgements

NSF CAREER/DMS-1255045 grant supported the work of Mark Vere Culp. The opinions and views expressed in this chapter are those of the authors and do not reflect the opinions or views at the NSF.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mark Vere Culp
    • 1
  • Kenneth Joseph Ryan
    • 1
  • George Michailidis
    • 2
  1. 1.West Virginia UniversityDepartment of StatisticsMorgantownUSA
  2. 2.University of FloridaDepartment of StatisticsGainesvilleUSA

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