Group = svmclassify(SVMStruct,Sample) Group = svmclassify(SVMStruct,Sample,'Showplot',true)

Description

Group = svmclassify(SVMStruct,Sample) classifies
each row of the data in Sample, a matrix of data,
using the information in a support vector machine classifier structure SVMStruct,
created using the svmtrain function.
Like the training data used to create SVMStruct, Sample is
a matrix where each row corresponds to an observation or replicate,
and each column corresponds to a feature or variable. Therefore, Sample must
have the same number of columns as the training data. This is because
the number of columns defines the number of features. Group indicates
the group to which each row of Sample has been
assigned.

Group = svmclassify(SVMStruct,Sample,'Showplot',true) plots
the Sample data in the figure created using
the Showplot property with the svmtrain function.
This plot appears only when the data is two-dimensional.

Input Arguments

SVMStruct

Support vector machine classifier structure created using the svmtrain function.

Sample

A matrix where each row corresponds to an observation or replicate,
and each column corresponds to a feature or variable. Therefore, Sample must
have the same number of columns as the training data. This is because
the number of columns defines the dimensionality of the data space.

Showplot

Describes whether to display a plot of the classification. Displays
only for 2-D problems. Follow with a Boolean argument: true to
display the plot, false to give no display.

Output Arguments

Group

Column vector with the same number of rows as Sample.
Each entry (row) in Group represents the class
of the corresponding row of Sample.

Find a line separating the Fisher iris data on versicolor and virginica species, according to the petal length and petal width measurements. These two species are in rows 51 and higher of the data set, and the petal length and width are the third and fourth columns.

where s_{i} are the support
vectors, α_{i} are the
weights, b is the bias, and k is
a kernel function. In the case of a linear kernel, k is
the dot product. If c ≥ 0, then x is classified
as a member of the first group, otherwise it is classified as a member
of the second group.

[1] Kecman, V., Learning and Soft Computing,
MIT Press, Cambridge, MA. 2001.

[2] Suykens, J.A.K., Van Gestel, T., De Brabanter,
J., De Moor, B., and Vandewalle, J., Least Squares Support Vector
Machines, World Scientific, Singapore, 2002.

[3] Scholkopf, B., and Smola, A.J., Learning
with Kernels, MIT Press, Cambridge, MA. 2002.

[4] Cristianini, N., and Shawe-Taylor, J.
(2000). An Introduction to Support Vector Machines and Other Kernel-based
Learning Methods, First Edition (Cambridge: Cambridge University Press). http://www.support-vector.net/