Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Find classification margins for support vector machine (SVM) classifier

`m = margin(SVMModel,TBL,ResponseVarName)`

`m = margin(SVMModel,TBL,Y)`

`m = margin(SVMModel,X,Y)`

returns the classification
margins (`m`

= margin(`SVMModel`

,`TBL`

,`ResponseVarName`

)`m`

) for the trained support vector
machine (SVM) classifier `SVMModel`

using the sample data in
table `TBL`

and the class labels in
`TBL.ResponseVarName`

.

`m`

is returned as a numeric vector with the same length as
`Y`

. The software estimates each entry of
`m`

using the trained SVM classifier
`SVMModel`

, the corresponding row of
`X`

, and the true class label
`Y`

.

For binary classification, the software defines the margin for
observation *j*, *m _{j}*, as

$${m}_{j}=2{y}_{j}f({x}_{j}),$$

where *y _{j}* ∊ {-1,1}, and

[1] Christianini, N., and J. C. Shawe-Taylor. *An
Introduction to Support Vector Machines and Other Kernel-Based Learning
Methods*. Cambridge, UK: Cambridge University Press, 2000.

`ClassificationSVM`

| `CompactClassificationSVM`

| `edge`

| `fitcsvm`

| `loss`

| `predict`