Abstract
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.
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© 2000 Springer Science+Business Media New York
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Vapnik, V.N. (2000). Methods of Function Estimation. In: The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3264-1_7
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DOI: https://doi.org/10.1007/978-1-4757-3264-1_7
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-3160-3
Online ISBN: 978-1-4757-3264-1
eBook Packages: Springer Book Archive