Abstract
Kernel density estimation is a tool which allows the statistician to construct a density on any sample of data. Recent references on density estimation with a probabilistic background are numerous (e.g., books by Hand 1982, Silverman 1986, Devroye 1985). These methods compute a weighted sum of kernels centered on each data point.
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© 2000 Springer-Verlag Berlin Heidelberg
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Rasson, JP., Lissoir, S. (2000). Discrimination: Assigning Symbolic Objects to Classes. In: Bock, HH., Diday, E. (eds) Analysis of Symbolic Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57155-8_10
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DOI: https://doi.org/10.1007/978-3-642-57155-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66619-6
Online ISBN: 978-3-642-57155-8
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