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Erschienen in:
2000 | OriginalPaper | Buchkapitel
Methods of Function Estimation
verfasst von : Vladimir N. Vapnik
Erschienen in: The Nature of Statistical Learning Theory
Verlag: Springer New York
Enthalten in: Professional Book Archive
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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.