gaussian<T>
Description
Gaussian<T> is a Kernel. Specifically, it is a Mercer Kernel. If k is an object of class gaussian<T>, and u and v are objects of class T, then k(u,v) returns
where 
is the width of the kernel. Figure 1 shows a Gaussian kernel.
Figure 1: A two dimensional (left) and three-dimensional (right) plot of a single Gaussian kernel located at the origin.
The Gaussian kernel is a good choice for a great deal of applications, although sometimes it is remarked as being overused [1].
Example
std::vector< double > u(10); std::vector< double > v(10); gaussian< std::vector< double > > kernel(1.0); std::cout << kernel( u, v ) << std::endl;
Definition
Defined in the KML header <kml/gaussian.hpp>.
Template Parameters
| Parameter | Description | Default |
| T | The gaussian argument type | |
Model of
Type requirements
T must be a vector type or a numeric type; distance_squared<T> should evaluate.
Members
| Member | Where defined | Description |
| gaussian() | Default Constructible | The default constructor |
| result_type | Input value | The type of the result: input_value<T> |
Notes
See also
Mercer Kernel, linear, hermitian, polynomial, sigmoid
References
Bernhard Schölkopf and Alexander Smola. Learning with Kernels. Adaptive Computation and Machine Learning. The MIT Press, Cambridge, Massachusetts, USA, 2002. ISBN 0-262-19475-9.
http://www.learning-with-kernels.org