Classify using support vector machine (SVM)
svmclassify
will be removed in a future release.
See fitcsvm
, ClassificationSVM
,
and CompactClassificationSVM
instead.
Group = svmclassify(SVMStruct,Sample)
Group = svmclassify(SVMStruct,Sample,'Showplot',true)
classifies
each row of the data in Group
= svmclassify(SVMStruct
,Sample
)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.
plots
the Group
= svmclassify(SVMStruct
,Sample
,'Showplot
',true)Sample
data in the figure created using
the Showplot
property with the svmtrain
function.
This plot appears only when the data is twodimensional.

Support vector machine classifier structure created using the 

A matrix where each row corresponds to an observation or replicate,
and each column corresponds to a feature or variable. Therefore, 

Describes whether to display a plot of the classification. Displays
only for 2D problems. Follow with a Boolean argument: 

Column vector with the same number of rows as 
The svmclassify
function uses results from svmtrain
to
classify vectors x according to the following equation:
$$c={\displaystyle \sum _{i}{\alpha}_{i}k({s}_{i},x)+b},$$
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 ShaweTaylor, J.
(2000). An Introduction to Support Vector Machines and Other Kernelbased
Learning Methods, First Edition (Cambridge: Cambridge University Press). http://www.supportvector.net/