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Error Functions
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PRE
AI
DOI：10.1093/oso/9780198538493.003.0006.png)
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Abstract
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Abstract
In previous chapters we have made use of the sum-of-squares error function, which was motivated primarily by analytical simplicity. There are many other possible choices of error function which can also be considered, depending on the particular application. In this chapter we shall describe a variety of different error functions and discuss their relative merits. For regression problems we shall see that the basic goal is to model the conditional distribution of the output variables, conditioned on the input variables. This motivates the use of a sum-of-squares error function, and several important properties of this error function will be explored in some detail.
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