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# svmclassify

Classify using support vector machine (SVM)

## Syntax

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.

## Examples

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### Classify an Observation Using a Trained SVM Classifier.

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.

```load fisheriris
xdata = meas(51:end,3:4);
group = species(51:end);
figure;
svmStruct = svmtrain(xdata,group,'ShowPlot',true);
```

Classify two new flowers, one with petal length 5 and petal width 2 and the other with petal length 4 and petal width 1.5.

```Xnew = [5 2; 4 1.5];
species = svmclassify(svmStruct,Xnew,'ShowPlot',true)
hold on;
plot(Xnew(:,1),Xnew(:,2),'ro','MarkerSize',12);
hold off
```
```species =

'virginica'
'versicolor'

```

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### Algorithms

The svmclassify function uses results from svmtrain to classify vectors x according to the following equation:

$c=\sum _{i}{\alpha }_{i}k\left({s}_{i},x\right)+b,$

where si are the support vectors, αi 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.

## References

[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/