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book chapter · 1995

Probability Density Estimation

Chris Bishop

Cited by 8Abstract and details only

Abstract

Abstract Density estimation can also be applied to unlabelled data (that is data without any class labels) where it has a number of applications. In the context of neural networks it can be applied to the distribution of data in the input space as part of the training process for radial basis function networks (Section 5.9), and to provide a method for validating the outputs of a trained neural network (Bishop, 1994b). In Chapter 6, techniques for density estimation are combined with neural network models to provide a general framework for modelling conditional density functions.

Cite this paper

APA 7

Bishop, C. (1995). Probability Density Estimation. https://doi.org/10.1093/oso/9780198538493.003.0002

MLA 9

Bishop, Chris. “Probability Density Estimation.” 1995, pp. 33–76, https://doi.org/10.1093/oso/9780198538493.003.0002.

Chicago

Bishop, Chris. 1995. “Probability Density Estimation.” https://doi.org/10.1093/oso/9780198538493.003.0002.

Harvard

Bishop, C. (1995) ‘Probability Density Estimation’. Available at: https://doi.org/10.1093/oso/9780198538493.003.0002.

BibTeX
@incollection{bishop1995probability,
  title = {{Probability Density Estimation}},
  author = {Bishop, Chris},
  pages = {33--76},
  year = {1995},
  doi = {10.1093/oso/9780198538493.003.0002}
}

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