(Removed) Train support vector machine classifier
svmtrain
has been removed. Use fitcsvm
, ClassificationSVM
, and CompactClassificationSVM
instead. For more information, see Compatibility Considerations.
SVMStruct = svmtrain(Training,Group)
SVMStruct = svmtrain(Training,Group,Name,Value)
returns a structure, SVMStruct
= svmtrain(Training
,Group
)SVMStruct
, containing information about the trained
support vector machine (SVM) classifier.
returns a structure with additional options specified by one or more
SVMStruct
= svmtrain(Training
,Group
,Name,Value
)Name,Value
pair arguments.

Matrix of training data, where each row corresponds to an observation or replicate,
and each column corresponds to a feature or variable. 

Grouping variable, which can be a categorical, numeric, or logical vector, a cell
array of character vectors, or a character matrix with each row representing a class
label. Each element of 
Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as Name1,Value1,...,NameN,ValueN
.

Boolean specifying whether Default:  

Value of the box constraint If If Default:  

Value that specifies the size of the kernel matrix cache for the SMO training
method. The algorithm keeps a matrix with up to
Default:  

Kernel function
Default:  

Value that specifies the fraction of variables allowed to violate the
KarushKuhnTucker (KKT) conditions for the SMO training method. Set any value in
[0,1). For example, if you set TipSet this option to a positive value to help the algorithm converge if it is fluctuating near a good solution. For more information on KKT conditions, see Cristianini and ShaweTaylor [4]. Default:  

Method used to find the separating hyperplane. Options are:
Default:  

Parameters of the Multilayer Perceptron ( Default:  

Options structure for training.
 

Order of the polynomial kernel. Default:  

Scaling factor (sigma) in the radial basis function kernel. Default:  

Boolean indicating whether to plot the grouped data and separating line. Creates a plot only when the data has two columns (features). Default:  

Value that specifies the tolerance with which the KarushKuhnTucker (KKT) conditions are checked for the SMO training method. For a definition of KKT conditions, see KarushKuhnTucker (KKT) Conditions. Default: 

Structure containing information about the trained SVM classifier in the following fields:

To classify new data, use the result of training, SVMStruct
, with
the svmclassify
function.
[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). https://www.supportvector.net/
ClassificationSVM
 CompactClassificationSVM
 classify
 fitcsvm
 svmclassify