Abstract
With parametric methods, the forms of the underlying density functions are known, and are generally taken as Gaussian. However, these parametric forms do not always fit the probability densities encountered in practice. Most of the classical parametric densities are unimodal, whereas many practical problems involve multimodal densities. For arbitrary distributions of unknown densities, nonparametric methods need to be employed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anlauf, J.K., Biehl, M.: The AdaTron: an adaptive perceptron algorithm. Europhys. Lett. 10, 687–692 (1989)
Cortes, C., Vapnick, V.N.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Cybenko, G.: Approximations by superpositions of sigmoidal functions. Math Control Signal Syst 2, 303–314 (1989)
Gallant, S.I.: Perceptron-based learning algorithms. IEEE Trans. Neural Netw. 1, 179–191 (1990)
Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251–257 (1991)
Krauth, W., Mezard, M.: Learning algorithms with optimal stability in neural networks. J. Phys. A 20, 745–752 (1987)
Le Cun, Y.: Learning processes in an asymmetric threshold network. In: Bienenstock, E., Fogelman-Smith, F., Weisbuch, G. (eds.) Disordered Systems and Biological Organization, NATO ASI Series, F20. Springer-Verlag, Berlin (1986)
McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 7, 115–133 (1943)
Minsky, M.L., Papert, S.A.: Perceptrons. MIT Press, MA (1969)
Parker, D.: Learning Logic. Technical Report TR-87. MIT Center for Computational Research in Economics and Management Science, Cambridge, MA (1985)
Widrow, B., Hoff, M.E.: Adaptive switching circuits. In 1960 IRE WESCON Convention Record, part 4, pp. 96–104. IRE, New York (1960)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Dougherty, G. (2013). Nonparametric Learning. In: Pattern Recognition and Classification. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5323-9_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-5323-9_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5322-2
Online ISBN: 978-1-4614-5323-9
eBook Packages: Computer ScienceComputer Science (R0)