Discard support vectors for linear support vector machine (SVM) classifier
returns the trained, linear support vector machine (SVM) model
Mdl
= discardSupportVectors(MdlSV
)Mdl
. Both Mdl
and the trained, linear SVM
model MdlSV
are the same type of object. That is, they both are
either ClassificationSVM
objects or CompactClassificationSVM
objects. However, Mdl
and
MdlSV
differ in the following ways:
The Alpha
,
SupportVectors
, and SupportVectorLabels
properties are empty
([]
) in Mdl
.
If you display Mdl
, the software lists the
Beta
property
instead of Alpha
.
For a trained, linear SVM model, the SupportVectors
property is an
nsv-by-p
matrix. nsv is the number of support
vectors (at most the training sample size) and p is the
number of predictors, or features. The Alpha
and SupportVectorLabels
properties are vectors with
nsv elements. These properties
can be large for complex data sets containing many observations or examples. The
Beta
property is a
vector with p elements.
If the trained SVM model has many support vectors, use
discardSupportVectors
to reduce the amount of space
consumed by the trained, linear SVM model. You can display the size of the
support vector matrix by entering
size(MdlSV.SupportVectors)
.
predict
and resubPredict
estimate SVM scores
f(x), and subsequently label and estimate
posterior probabilities using
β is Mdl.Beta
and
b is Mdl.Bias
, that is, the
Beta
and Bias
properties of
Mdl
, respectively. For more details, see Support Vector Machines for Binary Classification.
ClassificationECOC
| ClassificationSVM
| CompactClassificationSVM
| discardSupportVectors
| fitcsvm
| templateSVM