Support vector machine template
returns
a support vector machine (SVM) learner template suitable for training
error-correcting output code (ECOC) multiclass models.t
= templateSVM()
If you specify a default template, then the software uses default values for all input arguments during training.
Specify t
as a binary learner, or one in a set of binary learners, in
fitcecoc
to train an ECOC multiclass
classifier.
returns
a template with additional options specified by one or more name-value
pair arguments.t
= templateSVM(Name,Value
)
For example, you can specify the box constraint, the kernel function, or whether to standardize the predictors.
If you display t
in the Command Window, then
all options appear empty ([]
), except those that
you specify using name-value pair arguments. During training, the
software uses default values for empty options.
By default and for efficiency, fitcecoc
empties the Alpha
, SupportVectorLabels
,
and SupportVectors
properties
for all linear SVM binary learners. fitcecoc
lists Beta
, rather than
Alpha
, in the model display.
To store Alpha
, SupportVectorLabels
, and
SupportVectors
, pass a linear SVM template that specifies storing
support vectors to fitcecoc
. For example,
enter:
t = templateSVM('SaveSupportVectors',true) Mdl = fitcecoc(X,Y,'Learners',t);
You can remove the support vectors and related values by passing the resulting
ClassificationECOC
model to
discardSupportVectors
.
[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.
[2] Fan, R.-E., P.-H. Chen, and C.-J. Lin. “Working set selection using second order information for training support vector machines.” Journal of Machine Learning Research, Vol 6, 2005, pp. 1889–1918.
[3] Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Second Edition. NY: Springer, 2008.
[4] Kecman V., T. -M. Huang, and M. Vogt. “Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance.” In Support Vector Machines: Theory and Applications. Edited by Lipo Wang, 255–274. Berlin: Springer-Verlag, 2005.
[5] Scholkopf, B., J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson. “Estimating the Support of a High-Dimensional Distribution.” Neural Comput., Vol. 13, Number 7, 2001, pp. 1443–1471.
[6] Scholkopf, B., and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, Adaptive Computation and Machine Learning. Cambridge, MA: The MIT Press, 2002.