Abstract
In this chapter we describe a class of learning methods that was developed separately in different fields—statistics and artificial intelligence—based on essentially identical models. The central idea is to extract linear combinations of the inputs as derived features, and then model the target as a nonlinear function of these features. The result is a powerful learning method, with widespread applications in many fields. We first discuss the projection pursuit model, which evolved in the domain of semiparametric statistics and smoothing. The rest of the chapter is devoted to neural network models.
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© 2001 Springer Science+Business Media New York
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Hastie, T., Friedman, J., Tibshirani, R. (2001). Neural Networks. In: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21606-5_11
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DOI: https://doi.org/10.1007/978-0-387-21606-5_11
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0519-2
Online ISBN: 978-0-387-21606-5
eBook Packages: Springer Book Archive