Methods of Function Estimation

  • Vladimir N. Vapnik
Part of the Statistics for Engineering and Information Science book series (ISS)


In this chapter we generalize results obtained for estimating indicator function (for the pattern recognition problem) to the problem of estimating real-valued functions (regressions). We introduce a new type of loss function (the so-called ε-insensitive loss function) that makes our estimates not only robust but also sparse. As we will see, in this and in the next chapter, the sparsity of the solution is very important for estimating dependencies in high-dimensional spaces using a large number of data.


Support Vector Loss Function Support Vector Regression Fourier Expansion Pattern Recognition Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Vladimir N. Vapnik
    • 1
  1. 1.Room 3-130AT&T Labs-ResearchRed BankUSA

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