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- W13954592 abstract "In this chapter, we get our first taste of real analysis, starting with some results on the approximations of functions in L 1. The literature on this is vast, and a lot of it is ancient. The problem is that f cannot be approximated in L 1 by μ n , the empirical measure, as the total variation distance between any density f and any atomic measure (like μ n ) is 1. Thus, the approximation itself must have a density. The kernel estimate provides this: it smooths the empirical measure μ n . This section has no combinatorial contributions, but develops just enough of the function approximation material to understand the remaining chapters.KeywordsKernel Density EstimateKernel EstimateUniform DensityTotal Variation DistanceEpanechnikov KernelThese 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." @default.
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- W13954592 date "2001-01-01" @default.
- W13954592 modified "2023-10-18" @default.
- W13954592 title "The Kernel Density Estimate" @default.
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- W13954592 doi "https://doi.org/10.1007/978-1-4613-0125-7_9" @default.
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