Abstract
Quite commonly, we are faced with the problem of taking a vector x = (x1, … , xn) of inputs and producing a vector y = (y1, … , ym) of outputs. For example, in a classification problem, the x1, … , xn may be characteristics of an item to be classified, and the corresponding output could be a single y, the class label for that item. Hence, the task is to uncover a function g such that y = g(x). Of course, the mapping g may be nonlinear. Generally, we are satisfied if we can approximate the ‘true’ function g sufficiently accurately by a function f of some particular form, e.g., polynomial in several variables, where f has coefficients or parameters whose values we need to determine.
Keywords
These 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Brabazon, A., O’Neill, M., McGarraghy, S. (2015). Neural Networks for Supervised Learning. In: Natural Computing Algorithms. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43631-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-43631-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43630-1
Online ISBN: 978-3-662-43631-8
eBook Packages: Computer ScienceComputer Science (R0)